# Hallucinations in Large Multilingual Translation Models

Nuno M. Guerreiro1,3 Duarte M. Alves1,3 Jonas Waldendorf 4
Barry Haddow4 Alexandra Birch4 Pierre Colombo5 André F. T. Martins1,2,3
1Instituto de Telecomunicações, Lisbon, Portugal 2Unbabel, Lisbon, Portugal
3Instituto Superior Técnico, University of Lisbon, Portugal
4School of Informatics, University of Edinburgh
5MICS, CentraleSupélec, Université Paris-Saclay
<EMAIL_ADDRESS>
###### Abstract

Large-scale multilingual machine translation systems have demonstrated
remarkable ability to translate directly between numerous languages, making
them increasingly appealing for real-world applications. However, when
deployed in the wild, these models may generate hallucinated translations
which have the potential to severely undermine user trust and raise safety
concerns. Existing research on hallucinations has primarily focused on small
bilingual models trained on high-resource languages, leaving a gap in our
understanding of hallucinations in massively multilingual models across
diverse translation scenarios. In this work, we fill this gap by conducting a
comprehensive analysis on both the M2M family of conventional neural machine
translation models and ChatGPT, a general-purpose large language model (LLM)
that can be prompted for translation. Our investigation covers a broad
spectrum of conditions, spanning over 100 translation directions across
various resource levels and going beyond English-centric language pairs. We
provide key insights regarding the prevalence, properties, and mitigation of
hallucinations, paving the way towards more responsible and reliable machine
translation systems.

##  1 Introduction

Recent advancements in large-scale multilingual machine translation have
brought us closer to realizing a universal translation system: a single model
capable of handling numerous languages and translation directions (Aharoni et
al., 2019; Arivazhagan et al., 2019; Fan et al., 2020; Zhang et al., 2020;
Wenzek et al., 2021; Goyal et al., 2022; NLLB Team et al., 2022).
Concurrently, general-purpose large language models (LLMs) have exhibited a
remarkable ability to generalize to new tasks, including translation, where
they are becoming increasingly stronger (Brown et al., 2020; Chowdhery et al.,
2022; Hendy et al., 2023). Compared to traditional bilingual models, these
systems can offer significant performance improvements and greatly simplify
engineering efforts, as a single model can be used for all language pairs
(Arivazhagan et al., 2019). As a result, they are an increasingly attractive
choice for real-world applications. However, when deployed in the wild, these
models may still generate hallucinations: highly pathological translations
that can severely damage user trust and pose serious safety concerns (Perez et
al., 2022; Kumar et al., 2022).

The problem of hallucinations has long been recognized by researchers (Ji et
al., 2022), and recent studies have contributed towards better understanding,
detection and mitigation of these pathological translations. However, these
studies have been conducted on small bilingual models (<100M parameters)
trained on a single English-centric high-resource language pair (Raunak et
al., 2021; Ferrando et al., 2022; Guerreiro et al., 2022b, a; Dale et al.,
2022; Xu et al., 2023). This leaves a knowledge gap regarding the prevalence
and properties of hallucinations in large-scale translation models across
different translation directions, domains and data conditions.

In this work, we aim to fill this gap by investigating hallucinations on two
different classes of models. The first and main class in our analysis is the
de facto standard approach of massively multilingual supervised models: we use
the M2M-100 family of multilingual NMT models (Fan et al., 2020), which
includes the largest open-source multilingual NMT model with 12B parameters.
The second class is the novel and promising approach of leveraging generative
LLMs for translation. Contrary to conventional NMT models, these models are
trained on massive amounts of monolingual data in many languages, with a
strong bias towards English, and do not require parallel data. In our
analysis, we use ChatGPT111https://openai.com/blog/chatgpt; this system has
not been documented, so details of training data and training regime are
unknown., a LLM that has been shown to achieve surprisingly high translation
quality over a wide range of language pairs (Hendy et al., 2023; Peng et al.,
2023).

We organize our study by analyzing the two prevalent types of hallucinations
in NMT considered in the literature: hallucinations under perturbation and
natural hallucinations (Lee et al., 2018; Raunak et al., 2021; Guerreiro et
al., 2022b). Firstly, we study hallucinations under perturbation and evaluate
whether these translation systems are robust to source-side artificial
perturbations. While previous studies have found that these perturbations
(e.g., spelling errors and capitalization mistakes) can reliably induce
hallucinations (Lee et al., 2018; Raunak et al., 2021), it is not clear
whether those conclusions hold for large multilingual models. Secondly, we
comprehensively investigate natural hallucinations, and evaluate their
prevalence and properties in the outputs of the massively multilingual M2M
models on a vast range of conditions, spanning from English-centric to non-
English-centric language pairs, translation directions with little
supervision, and specialized sensitive domains where hallucinations have
devastating impact on user trust (e.g., medical data). Finally, we study a
hybrid setup where other translation systems can be requested as fallback
systems when an original system hallucinates, with the aim of mitigating
hallucinations and improving overall translation quality.

Our analysis reveals several key insights on the prevalence and properties of
hallucinations, including:

  * •

multilingual models predominantly struggle with hallucinations in low-resource
language pairs and translating out of English, with hallucination rates well
above 10% for some translation directions;

  * •

hallucinations in low-resource language pairs can manifest toxic patterns that
can be traced back to the training data, posing serious safety issues;

  * •

smaller distilled models can mitigate hallucinations by incorporating modeling
choices that discourage them, such as leveraging less potent shallow decoders
that rely more on the encoder representations, and reducing bias towards
higher-resource language pairs through uniform sampling of translation
directions during distillation;

  * •

ChatGPT produces hallucinations that are qualitatively different from those of
conventional translation models, mostly consisting of off-target translations,
overgeneration, and even failed attempts to translate;

  * •

hallucinations are sticky and hard to reverse with models that share the same
training data and architecture, whereas employing more diverse models as
fallback systems can substantially improve overall translation quality and
eliminate pathologies like oscillatory hallucinations.

We release all our code and make available over a million translations in more
than 100 translation directions to spur future research.222All resources will
be made available in https://github.com/deep-spin/lmt_hallucinations.

##  2 Background

###  2.1 Large Multilingual Language Models

Massively multilingual neural machine translation has recently emerged as a
powerful paradigm for building machine translation systems that can handle
numerous languages (Akhbardeh et al., 2021; Wenzek et al., 2021; NLLB Team et
al., 2022; Siddhant et al., 2022; Bapna et al., 2022; Chowdhery et al., 2022).
These systems aim to translate directly with a single model for multiple
language pairs without relying on any pivot language.

The dominant strategy for achieving these systems is to train large
multilingual models on vast amounts of parallel data often obtained through a
combination of data mining and data augmentation strategies, such as
backtranslation (Sennrich et al., 2016; Edunov et al., 2018). Compared to
classic bilingual models, the multilinguality of these systems results in
significant improvements, particularly for low-resource and non-English-
centric language pairs, as these benefit the most from multilingual transfer
(Arivazhagan et al., 2019; Fan et al., 2020).

As an alternative, a novel and promising strategy is to leverage the emergent
capabilities of large language models (LLMs). These systems are pretrained on
massive nonparallel corpora and can be prompted to solve arbitrary tasks
(Radford et al., 2019; Brown et al., 2020). In fact, this approach has led to
impressive results across a wide variety of NLP tasks (Chowdhery et al., 2022;
Zhang et al., 2022). Translation is no exception: LLMs can produce fluent and
adequate translations, especially for high-resource English-centric language
pairs, that are competitive with those of dedicated supervised translation
models (Vilar et al., 2022; Peng et al., 2023; Garcia et al., 2023; Hendy et
al., 2023; Bawden and Yvon, 2023).

###  2.2 Hallucinations in Machine Translation

Hallucinations lie at the extreme end of translation pathologies and present a
critical challenge in machine translation, as they can severely compromise the
safety and reliability of real-world applications.

Importantly, hallucinations in machine translation are unlike hallucinations
in other natural language generation tasks (e.g., abstractive summarization
and generative question answering) (Ji et al., 2022). While, for these other
tasks, models often produce hallucinated outputs (Falke et al., 2019; Cao et
al., 2022; Manakul et al., 2023), hallucinations in machine translation,
possibly attributed to the more closed-ended nature of the task, are
substantially rarer and hard to observe in clean, unperturbed data. This has
led several previous studies to examine their properties by creating
artificial scenarios where hallucinations are more likely to occur (e.g.,
introducing perturbations in the source text (Lee et al., 2018) or noise in
the training data (Raunak et al., 2021)). To distinguish these two scenarios,
hallucinations in machine translation are categorized into two types (Raunak
et al., 2021): hallucinations under perturbation and natural hallucinations.

##### Hallucinations under perturbation.

A model generates a hallucination under perturbation when it produces a
significantly lower quality translation for a slightly perturbed input
compared to the original input (Lee et al., 2018). Hallucinations under
perturbation explicitly reveal the lack of robustness of translation systems
to perturbations in the source text (e.g., misspellings or capitalization
errors) by finding translations that undergo significant negative shifts in
quality due to these changes.

##### Natural hallucinations.

Contrary to hallucinations under perturbations, these translations occur
naturally without any perturbation. As a result, natural hallucinations are
rare and challenging to study. In this work, we follow the taxonomy introduced
in Raunak et al. (2021) and later extended in Guerreiro et al. (2022b). Under
this taxonomy, hallucinations are translations that contain content that is
detached from the source text. To distinguish between different types of
hallucinations, they can be categorized as largely fluent detached
hallucinations or oscillatory hallucinations. The former refers to
translations that bear minimal or no relation at all to the source, while the
latter refers to inadequate translations that contain erroneous repetitions of
words and phrases.

##  3 Experimental Suite

In this section, we provide an overview of the models, datasets and evaluation
metrics used throughout our study.

###  3.1 Models

We focus on two classes of models: (i) conventional supervised multilingual
NMT models, and (ii) LLMs that can be prompted for translation.

For the supervised multilingual NMT models, we use the transformer-based
(Vaswani et al., 2017) M2M-100 family of models (Fan et al., 2020), which
consists of three variants with different sizes: M2M (S) with 418M parameters,
M2M (M) with 1.2B parameters, and M2M (L) — the largest available open-source
multilingual NMT model — with 12B parameters. These models were trained on a
many-to-many parallel dataset comprising 7.5B sentences crawled from the web,
and support 100 languages and thousands of translation directions. We also
experiment with SMaLL100 (Mohammadshahi et al., 2022), a shallow multilingual
NMT model with 330M parameters obtained via distillation of M2M (L). Unlike
the M2M models, SMaLL100 was trained on a much smaller training set with
uniform sampling across all language pairs to reduce the bias towards high-
resource languages: only 100k parallel sentences from the original M2M
training data were used for each translation direction, for a total of 456M
parallel sentences. For decoding, we run beam search with a beam size of 4.
All experiments were run on fairseq (Ott et al., 2019).

As for the alternative strategy using LLMs, we use ChatGPT
(gpt-3.5-turbo)333https://platform.openai.com/docs/models/gpt-3-5; we used the
API in March, 2023. , a variant of GPT3.5 — a GPT-family (Radford and
Narasimhan, 2018; Radford et al., 2019; Brown et al., 2020) large-scale model
with 175B parameters — that has been fine-tuned with human feedback in the
style of InstructGPT (Ouyang et al., 2022). ChatGPT has been shown to achieve
impressive results for multiple multilingual NLP tasks, including translation
(Kocmi and Federmann, 2023; Lu et al., 2023; Fu et al., 2023; Hendy et al.,
2023; Peng et al., 2023). To generate translations, we use the zero-shot
prompt template used in Hendy et al. (2023) and keep the generation parameters
as the default API parameters.444We encountered several API/server errors when
prompting ChatGPT for translation with temperature 0, particularly for low-
resource language pairs and languages with lower coverage scripts. Those
errors are alleviated, although not entirely eliminated, when the default
parameters are used.

###  3.2 Datasets

We carefully selected datasets based on two main criteria: their familiarity
to researchers and practitioners, and the avoidance of train/test overlap for
the M2M models.555ChatGPT’s training data is not publicly available. As such,
we cannot guarantee that it has not been exposed to the data we use in our
analysis. To this end, we chose to use premier translation benchmarks:
Flores-101 (Goyal et al., 2022), WMT and TICO (Anastasopoulos et al., 2020).
Flores-101 is a high-quality multi-parallel dataset that consists of Wikipedia
text in 101 languages and allows for the assessment of hallucinations across a
vast range of translation directions; we join the dev and devtest subsets for
evaluation. For WMT, we used the same benchmarks as those used in the original
M2M paper evaluation suite, as these were explicitly removed from the training
data. Additionally, we selected recent WMT test sets from the WMT21 and WMT22
campaigns as they were released after the models were trained. In contrast to
these general-purpose datasets, TICO is a specialized medical-domain
multilingual benchmark that includes COVID-19 related data, such as medical
papers and news articles; we join the dev and test sets. Full details about
the datasets can be found in Appendix B.

###  3.3 Evaluation Metrics

Throughout our work, we focus mainly on sentence-level evaluation. Our main
lexical metric is spBLEU (Goyal et al., 2022),666We use spBLEU as implemented
in Sacrebleu (Post, 2018): nrefs:1|case:mixed|eff:yes|tok:flores101|
smooth:exp|version:2.3.1. as it has been widely employed in works on massively
multilingual translation (Fan et al., 2020; Wenzek et al., 2021; Mohammadshahi
et al., 2022; NLLB Team et al., 2022) and offers fairer evaluation for low-
resource languages compared to BLEU (Papineni et al., 2002). Moreover, we
follow the most recent MT metrics shared-task recommendations (Freitag et al.,
2022) and also adopt neural metrics. We use the latest reference-based and
reference-free COMET variants: COMET-22 (Rei et al., 2022a) and CometKiwi (Rei
et al., 2022b). Lastly, we use the cross-lingual encoder LaBSE (Feng et al.,
2022) to obtain sentence similarity scores, as these have been successfully
employed in prior research on detection of natural hallucinations (Guerreiro
et al., 2022a; Dale et al., 2022).

##  4 Hallucinations under Perturbation

We start our analysis by focusing on artificially created hallucinations. We
first provide an overview of our experimental setting, focusing on the
construction of the perturbed data and detection approach. Then, we present
our results and analyze the properties of these hallucinations across
different resource levels and models.

Model | Low Resource |  | Mid Resource |  | High Resource
---|---|---|---|---|---
LP Fraction |  Rate (%) |  | LP Fraction |  Rate (%) |  |  LP Fraction |  Rate (%)
SMaLL100 |  2/7 |  $0.213_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  2/19 |  $0.009_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  1/5 |  $0.017_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
M2M (S) |  5/7 |  $0.261_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  11/19 |  $0.140_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  0/5 |  $0.000_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
M2M (M) |  3/7 |  $0.083_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  6/19 |  $0.035_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  0/5 |  $0.000_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
M2M (L) |  4/7 |  $0.296_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  3/19 |  $0.017_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  0/5 |  $0.000_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
1-9[.4pt/2pt] ChatGPT |  4/7 |  $0.059_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  10/19 |  $0.183_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  0/5 |  $0.000_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>

Table 1: Fraction of languages for which models produces at least one
hallucination under perturbation, and average hallucination rate (and median,
in subscript) across all languages at each resource level.

Figure 1: Heatmap of hallucination rates for each model in the languages
considered. Pattern-filled cells indicate at least one hallucination under
perturbation for a given model-language pair.

###  4.1 Evaluation Setting

##### Perturbations.

To construct the perturbed source sequences, we apply the same minimal
perturbations used in Xu et al. (2023): misspeling of words, insertion of
frequent tokens in the beginning of the source sequence, and capitalization
errors. For full details on the construction of the perturbed data, refer to
Appendix C.1.

##### Translation directions.

We use the Flores dataset for these experiments, and focus specifically on
translation out of English. We selected all bridge languages777In the M2M
paper (Fan et al., 2020), a bridge language is defined as a language that
connects languages across language groupings (e.g., Romance, Slavic languages)
and is mined against all other bridge languages. It is usually one of the most
resourced languages within a language grouping and its purpose is to reduce
the number of bitext pairs while preserving translation directions of
practical interest., as well as additional low-resource languages that were
underrepresented among bridge languages. Overall, we generate translations for
31 different language pairs (LPs). We present the language pairs and more
details on our choice of languages in Appendix C.1.

##### Detection.

Our detection approach is inspired by that of previous works on hallucinations
under perturbation (Lee et al., 2018; Raunak et al., 2021; Ferrando et al.,
2022; Xu et al., 2023). The algorithm is a simple 2-rule process: we fix (i) a
minimum threshold quality score for the original translations, and (ii) an
extremely low maximum quality score for the perturbed translations. A model
generates a hallucination under perturbation when both translations meet the
thresholds. Crucially, rule (i) ensures that low-quality translations for
unperturbed sources are not considered as candidates for hallucinations under
perturbation.888Note that low-quality translations for unperturbed sources
fall under the scope of the study on natural hallucinations, that follows in
subsequent sections of the paper.

We extend this algorithm to handle multiple models and language pairs by
adapting rule (i). We first obtain source sentences for which all models
produce translations that meet a minimum quality threshold (spBLEU > 9). Then,
we sort them according to average quality across the different models, and
select the top 20% as candidates. Finally, we apply rule (ii) and set the
threshold to spBLEU < 3\. We selected both thresholds based on the choices
made in previous works (Raunak et al., 2021; Ferrando et al., 2022; Xu et al.,
2023).

This approach ensures a fixed sample size across different language pairs, and
that the sentences analyzed for each language pair are consistent across all
models. Moreover, it allows us to effectively detect hallucinations under
perturbation across multiple models in a multilingual scenario in a scalable
manner, while accounting for the unique quality trends observed across
different models and languages.999Note that detection of hallucinations under
perturbation does not explicitly target detachment from the source text. We
provide a broader discussion on the difference between this detection approach
and that of natural hallucinations (introduced later in Section 5.1) in
Appendix C.2.

###  4.2 Results

We show aggregated results in Table 4 and language-specific results in Figure
1. Overall, they reveal that perturbations have the potential to trigger
hallucinations under perturbation, even in larger models. In what follows, we
highlight several noteworthy trends found in our results.

##### Average hallucination rates generally decrease with increasing resource
levels.

Table 4 shows that all models, with the exception of ChatGPT that we analyze
separately below, exhibit lower hallucination rates as resource levels
increase. This is expected and suggests that models are better equipped to
handle source-side perturbations for language pairs with more parallel data
during training. In fact, hallucinations under perturbation for high-resource
languages are almost non-existent. However, Figure 1 reveals variability
across languages, and even within the models in the same family that have been
trained on the same data. For instance, when translating to Asturian (ast),
M2M (L) and its distilled version SMaLL100 have significantly higher
hallucination rates than the smaller M2M (S). Thus, hallucinations under
perturbation may emerge in other non-trivial ways unrelated to the training
data.

#####  SMaLL100 exhibits lower hallucination rates than its teacher model M2M
(L).

Recall that SMaLL100 was trained using uniform sampling across all language
pairs to prevent bias towards higher resourced language pairs. The results in
Table 4 may reflect one positive outcome from such approach: despite being
much smaller than M2M (L), SMaLL100 hallucinates less and for fewer languages
than its teacher model for low- and mid-resource language pairs.

##### Hallucinations under perturbation are not correlated with the quality of
original translations.

The common approach for detection of hallucinations under perturbation (see
Section 4.1) raises an interesting question: _are the original source
sentences for which models produce higher quality translations less likely to
lead to hallucinations when perturbed?_ Our analysis found a very weak
correlation (according to Pearson correlation; see Appendix C.2) between
hallucinations under perturbation and spBLEU scores for the original
unperturbed sources across all models. This indicates that even minimal
perturbations in the source text can cause models to undergo significant
shifts in translation quality.

#####  ChatGPT exhibits different hallucination patterns from conventional
translation models.

Table 4 shows that, contrary to traditional models, ChatGPT generates more
hallucinations for mid-resource languages than for low-resource languages. In
fact, it surprisingly produces fewer hallucinations for low-resource languages
than any other model. Moreover, ChatGPT’s hallucinations are qualitatively
different from those of other models: they often consist of off-target
translations,101010We perform automatic language identification using the
fasttext (Joulin et al., 2016) LID model lid.176.bin. overgeneration, or even
failed attempts to translate (e.g., “This is an English sentence, so there is
no way to translate it to Vietnamese”; we provide further examples in Appendix
C.2). Furthermore, unlike traditional NMT models that frequently produce
oscillatory character hallucinations, ChatGPT does not generate any such
hallucinations under perturbation. This is further evidence that translation
errors, even severely critical ones, obtained via prompting a LLM are
different from those produced by traditional machine translation models (Vilar
et al., 2022; Garcia et al., 2023; Hendy et al., 2023; Bawden and Yvon, 2023).

Interestingly, we also found that the vast majority of the hallucinations can
be reversed with further sampling from the model.111111We also found this to
be the case with a one-shot prompt. This connects to findings in Guerreiro et
al. (2022b); Manakul et al. (2023): as with traditional NMT models,
hallucinations with a LLM may not necessarily indicate model defect or
incapacity to generate adequate translations, and may just result from “bad
luck” during generation.

##  5 Natural Hallucinations

Let us now turn to investigating natural hallucinations.121212From now on, we
will use the terms natural hallucinations — both detached and oscillatory
hallucinations — and hallucinations interchangeably. We first provide an in-
depth overview of our evaluation setting, focusing on the scenarios and
detection methodology. Subsequently, we present a thorough analysis, exploring
diverse properties of natural hallucinations such as their different types,
the influence of translation direction, and prevalence of toxicity.

###  5.1 Evaluation Setting

##### Evaluation scenarios.

Analyzing massively multilingual translation models opens up several research
scenarios that have not been studied in previous works that focused solely on
bilingual models. We will take advantage of this opportunity and investigate
natural hallucinations in three different evaluation scenarios, studying more
than 100 translation directions in the main text alone.

We start with an English-centric scenario where we pair 32 different languages
with English for a total of 64 translation directions. Then, we study a non-
English-centric scenario inspired by Fan et al. (2020), where we explore 25
language pairs corresponding to real-world use cases of translation not
involving English (e.g., translating Greek directly to Turkish). Finally, we
assess the prevalence of hallucinations on sensitive medical data where they
can have a devastating impact on user trust. We pair 9 different languages
with English for a total of 18 directions. We present all the translation
directions investigated in these setups in Appendix D.1. We report results for
the first two setups using the Flores dataset in the main text and WMT in
Appendix D.2. For the final setup, we use the medical-domain TICO dataset.

##### Detection.

We integrate key findings from recent research on detection of hallucinations
and focus on two main detectors: ALTI+ (Ferrando et al., 2022) for detached
hallucinations, and top $n$-gram (TNG) (Raunak et al., 2021, 2022; Guerreiro
et al., 2022b) for oscillatory hallucinations.

ALTI+ evaluates the relative contributions of both source and target prefixes
to model predictions. As hallucinations are translations detached from the
source sequence, ALTI+ can effectively detect them by identifying sentences
with minimal source contribution. Notably, it faithfully reflects model
behavior and explicitly signals model detachment from the source text in any
translation direction (Ferrando et al., 2022). In previous works, this method
has been successfully employed to detect hallucinated toxicity in a
multilingual context in NLLB Team et al. (2022), and it has been validated on
human-annotated hallucinations in Dale et al. (2022), where it was
demonstrated that ALTI+ scores easily separate detached hallucinations from
other translations.131313We followed the recommendations in Guerreiro et al.
(2022b) and set model-based ALTI+ thresholds based on validation data where
the models are expected to perform well. Specifically, we obtained the lowest
0.02% — in line with natural hallucination rates reported in the literature
(Raunak et al., 2022) — of the ALTI+ score distributions for high-resource WMT
benchmarks. Additionally, to ensure further trustworthy, high-precision
measurements, we excluded detected candidates with LaBSE or CometKiwi scores —
as these have been also been validated for detection of human-annotated
detached hallucinations (Dale et al., 2022; Guerreiro et al., 2022a) —
exceeding the top 10% of scores on translations from the same WMT benchmarks.

TNG, on the other hand, is a straightforward, lightweight black-box heuristic
targeting oscillatory hallucinations. It works by comparing the count of the
top repeated translation $n$-gram to the count of the top repeated source
$n$-gram, ensuring the difference is at least $t$. This approach has been
validated on human-annotated hallucinations and found to identify oscillatory
hallucinations with perfect precision (Guerreiro et al., 2022b). We follow
previous work by using $n=4$ and $t=2$ (Raunak et al., 2021; Guerreiro et al.,
2022b) and excluding translations that meet the reasonable quality threshold
outlined in Section 4.1.141414Note that oscillatory hallucinations can be
simultaneously detected with ALTI+ and TNG.

##### Remark on Model Selection.

We rely on ALTI+, a model-based detector, for reliable detection of detached
hallucinations. Since we lack access to glass-box internal features from
ChatGPT, we exclude it from our model selection to ensure consistency in our
analysis. It is important to note that using alternative detectors could lead
to misleading results and create discrepancies between the evaluation
scenarios for ChatGPT and other models. Nonetheless, we will further examine
ChatGPT in Section 6, exploring various aspects such as the generation of
oscillatory hallucinations and translation quality in scenarios where other
models produce hallucinations.

Model | Low Resource |  | Mid Resource |  | High Resource
---|---|---|---|---|---
LP Fraction |  Rate (%) |  | LP Fraction |  Rate (%) |  |  LP Fraction |  Rate (%)
SMaLL100 |  14/16 |  $2.352_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  19/38 |  $0.055_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  1/10 |  $0.005_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
M2M (S) |  15/16 |  $15.20_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  22/38 |  $0.254_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  3/10 |  $0.025_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
M2M (M) |  14/16 |  $12.53_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  17/38 |  $0.110_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  2/10 |  $0.010_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
M2M (L) |  14/16 |  $11.22_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  11/38 |  $0.034_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  0/10 |  $0.000_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
Table 2: Fraction of LPs on the English-centric setup for which models produce at least one hallucination, and average hallucination rate (and median, in subscript) across all LPs at each resource level. Figure 2: Heatmap of the percentage of hallucinations detected with TNG (oscillatory hallucinations) among all hallucinations. Pattern-filled cells indicate at least one natural hallucination for a given model-LP combination. Model |  xx--en |  en--xx
---|---|---
SMaLL100 |  $0.221$ |  $1.022$
M2M (S) |  $1.756$ |  $6.152$
M2M (M) |  $2.290$ |  $4.110$
M2M (L) |  $2.483$ |  $3.169$
Table 3: Average hallucination rates (%) across all LPs at each direction
(into or out of English).

###  5.2 English-Centric Translation

We start by investigating natural hallucinations on English-centric language
pairs. We reveal key insights on how properties of hallucinations change
across resource levels, models and translation directions. We present
language-pair specific results in Appendix D.2.

##### Hallucinations in low-resource language pairs are not only more
frequent, but also distinct.

Table 2 shows that hallucinations occur frequently for low-resource
directions, with all M2M models exhibiting average hallucination rates
exceeding 10%. Furthermore, all models generate hallucinations for the vast
majority of low-resource language pairs. On what comes to the type of
hallucinations, Figure 2 demonstrates that, in contrast to mid- and high-
resource language pairs, oscillatory hallucinations are less prevalent, while
detached hallucinations occur more frequently in low-resource languages. This
reveals that models tend to rely less on the source context when translating
to or from low-resource languages. Importantly, although massive multilingual
models have significantly improved translation quality for low-resource
languages, these findings not only suggest that there is considerable room for
improvement, but also highlight potential safety concerns arising from
translations in these directions.

Model | Low Resource |  | Mid Resource |  | High Resource
---|---|---|---|---|---
LP Fraction |  Rate (%) |  | LP Fraction |  Rate (%) |  |  LP Fraction |  Rate (%)
SMaLL100 |  5/10 |  $2.160_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  6/13 |  $0.054_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  1/2 |  $0.025_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
M2M (S) |  10/10 |  $12.61_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  12/13 |  $0.467_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  1/2 |  $0.075_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
M2M (M) |  7/10 |  $12.22_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  7/13 |  $0.172_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  0/2 |  $0.000_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
M2M (L) |  6/10 |  $6.580_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  4/13 |  $0.077_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  0/2 |  $0.000_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
Table 4: Fraction of LPs on the non-English-centric setup for which models
produce at least one hallucination, and average hallucination rate (and
median, in subscript) across all LPs at each resource level.

#####  SMaLL100 consistently relies more on the source text than other models.

Despite having the smallest number of parameters, SMaLL100 shows remarkable
hallucination rates across low- and mid-resource language pairs, hallucinating
significantly less than its larger counterparts in low-resource settings.
These improved rates may be attributed not only to the uniform sampling of
language pairs during training, but also to architectural decisions. While
SMaLL100 shares a 12-layer encoder with the other models to process source
representations, it diverges by employing a shallow 3-layer decoder—instead of
a 12-layer decoder—and placing the target language code on the encoder side.
We hypothesize that this design encourages greater reliance on the more
complex encoder representations, reducing the likelihood of detachment from
the source. In fact, distinct patterns in ALTI+ scores (shown in Appendix D.2)
support this hypothesis: SMaLL100 consistently demonstrates higher source
contributions and similar patterns across all resource levels. In contrast,
M2M models show a greater tendency to rely less on the source, especially in
low-resource language pairs. Importantly, however, SMaLL100’s reduced
hallucination rates do not necessarily imply superior translation quality
compared to the other M2M models: we observed a strong correlation between M2M
models’ corpus-level COMET-22 scores and their respective hallucination rates
for low-resource languages, whereas, contrastingly, for SMaLL100 the
correlation is weak. This indicates that despite detaching less from the
source content, SMaLL100’s translations are not necessarily of higher quality
to those of other M2M models. This and other statistics can be found in the
Appendix D.2.

##### Scaling up models within the same model family leads to reduced
hallucination rates.

As shown in Table 2, increasing the size of the M2M family models results in
consistent reductions in hallucination rates. Relative improvements are more
pronounced for mid- and high-resource language pairs, with M2M (L) exhibiting
fewer hallucinations and hallucinating for fewer languages than all other
models.

##### Hallucinations are more frequent when translating out of English.

Table 3 demonstrates that models are significantly more prone to hallucinate
when translating out of English. In fact, in line with the observations of
Ferrando et al. (2022), we found that models tend to detach more from the
source text when translating out of English. This is evidenced by ALTI+ source
contributions being lower across all language pairs in this direction compared
to translating into English. Interestingly, we discovered that the translation
direction can also influence the properties of hallucinations: (i) over 90% of
off-target hallucinations occur when translating out of English, and (ii)
nearly all hallucinations into English for mid- and high-resource language
pairs are oscillatory.

Model | Low Resource |  | Mid Resource |  | High Resource
---|---|---|---|---|---
Flores |  TICO |  |  Flores |  TICO |  |  Flores |  TICO
SMaLL100 |  $0.448_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  $0.429_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  $0.062_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  $0.083_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  $0.008_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  $0.000_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
M2M (S) |  $7.778_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  $6.262_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  $0.087_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  $0.167_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  $0.017_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  $0.024_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
M2M (M) |  $3.484_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  $2.167_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  $0.268_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  $0.363_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  $0.008_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  $0.000_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
M2M (L) |  $1.008_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  $0.596_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  $0.031_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  $0.018_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  |  $0.000_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>|  $0.000_{\,{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
Table 5: Comparison between average hallucination rate (and median, in
subscript) for the same LPs at each resource level for Flores and TICO medical
data.

##### Toxic hallucinations pose substantial safety risks.

Toxic text in translations can emerge in the form of hallucinations (NLLB Team
et al., 2022). To assess the prevalence of toxic text in detected
hallucinations, we utilized the toxicity wordlists provided by NLLB Team et
al. (2022). We found that toxic text primarily appears in translations out of
English and almost exclusively affects low-resource language pairs. For
instance, over 1 in 8 hallucinations in Tamil contain toxic text.
Interestingly, these toxic hallucinations not only exhibit high lexical
overlap among them, but are repeated across models for multiple unique source
sentences. Moreover, they are not necessarily reduced by scaling up the model
size. These observations suggest that these hallucinations are likely to be
traced back to toxic patterns in the training data,151515Upon inspecting the
Common Crawl corpora that were used to create the training data, we found
reference translations that exactly match the toxic hallucinations. aligning
with observations in Raunak et al. (2021); Guerreiro et al. (2022b). Moreover,
we also found that these hallucinations can be propagated through model
distillation, as evidenced by SMaLL100 generating toxic hallucinations that
are copies of those of its teacher model. This underlines the necessity of
rigorously filtering training data to ensure safe and responsible use of these
models in real-world applications.

###  5.3 Beyond English-Centric Translation

We shift our focus to translation directions that do not involve English,
typically corresponding to directions with less supervision during training.
We present language-pair specific results in Appendix D.3.

##### Trends are largely similar to English-centric directions.

Table 4 reveals trends that largely mirror those observed in the English-
centric setup:161616Comparing absolute hallucination rates between the two
setups is not advised, as they involve different translation directions, which
may render such comparisons unreliable. (i) hallucinations are more frequent
in low-resource settings; (ii) SMaLL100 significantly outperforms the M2M
models in low-resource language pairs; and (iii) scaling up to M2M (L)
consistently yields substantial improvements over the smaller M2M models in
low- and mid-resource directions. Additionally, the trends related to
hallucination types also hold across the two setups: detached hallucinations
are more prevalent in low-resource settings, while oscillatory hallucinations
overwhelmingly dominate in mid- and high-resource directions (see Appendix
D.3).

##### Less supervised language pairs exhibit extremely high hallucination
rates.

As expected, models struggle more with hallucinations for directions with less
or even no supervision during training, such as ro-hy and af-zu. For instance,
M2M (M) hallucinates for nearly half of the translations in these directions.

###  5.4 Translation on Specialized Domains

We now turn to investigating hallucinations in data from the medical domain,
where they can have devastating consequences. Using the TICO dataset, we
compare hallucination rates with the Flores dataset for 18 translation
directions. We present language-pair specific results in Appendix D.4.

##### Hallucinations are not exacerbated under medical domain data.

Table 5 reveals that hallucination rates for the TICO medical data do not
consistently exceed those observed for the Flores Wikipedia data. This finding
diverges from previous works that investigated hallucinations for specialized
domain data (Wang and Sennrich, 2020; Müller et al., 2020). We hypothesize
that, in contrast with the smaller models typically trained on limited
datasets from a single domain used in those works, the concept of "domain
shift" may not be as pronounced for M2M models. These models are not only much
larger but, crucially, they are trained on a dataset containing over 7 billion
parallel sentences gathered from the web, which encompasses a broad array of
domains. This massive training set potentially mitigates the impact of domain
shift and, consequently, reduces its influence on hallucinations.

##  6 Mitigation of Hallucinations through Fallback Systems

Building upon our analysis on natural hallucinations in the previous section,
we now explore the potential of reducing hallucinations and enhancing overall
translation quality by employing a simple hybrid setup that can take advantage
of multiple systems with possible complementary strengths. Put simply, we
leverage an alternative system as a fallback when the primary original model
produces hallucinations. Our analysis in the main text is focused on the more
extensive English-centric setup. We provide results on the non-English-centric
setup in Appendix E.

###  6.1 Employing models of the same family as fallback systems

We begin by analyzing the performance of same-family models when employed as
fallback systems for one another (e.g., using SMaLL100, M2M (M), and M2M (L)
as fallbacks for M2M (S)).171717For simplicity, we consider the distilled
SMaLL100 as a model from the M2M family.

Figure 3: Reversal rates for oscillatory (Osc.) and detached (Det.)
hallucinations when using models of the same family as fallback systems.

(a) Translation quality.

(b) Prevalence of oscillatory hallucinations.

Figure 4: Fallback system analysis recurring to models of different families,
such as ChatGPT and NLLB. We analyse overall translation quality improvements
on the original model hallucinated translations (represented with dashed
lines) across different resource levels via COMET-22 scores in (a), and
overall prevalence of oscillatory hallucinations among the fallback
translations in (b).

##### Detached hallucinations are particularly sticky across M2M models.

Figure 3 reveals that when employing M2M models as fallback systems, reversal
rates—percentage of hallucinations from the original system that are corrected
by the fallback system—are consistently higher for oscillatory hallucinations
than for detached hallucinations. These findings not only corroborate those in
Guerreiro et al. (2022b), where oscillatory hallucinations were found to be
less related to model defects, but also further emphasize the close connection
between detached hallucinations and training data. This connection can help
explain their stickiness: since the M2M models share the same training data,
reversing these hallucinations using other same-family models as fallbacks is
more challenging. Interestingly, we also observe that M2M (L) particularly
struggles to reverse the detached hallucinations generated by its distilled
counterpart SMaLL100, suggesting that model defects can persist and be shared
during distillation.

##### Scaling up within the model family is not an effective strategy for
mitigating hallucinations.

In line with our analysis in Section 5.2, Figure 3 shows that reversal rates
using SMaLL100 as a fallback system are higher for detached hallucinations
than for oscillatory hallucinations. In fact, although SMaLL100 is a distilled
M2M model, its training data, training procedure, and architecture differ from
those of the M2M models. This distinction may make it more complementary as a
fallback system to other M2M models than simply scaling up within the same
model family. This suggests that merely increasing the scale of models within
the same family is not an effective strategy for mitigating hallucinations,
and exploring alternative models with different architectures and trained on
different data could yield more substantial improvements. In the next section,
we will analyze this alternative strategy.

###  6.2 Employing external models as fallback systems

Motivated by the findings from the previous section, we will now study how
models that are not from the M2M family can be employed to further mitigate
hallucinations and improve translation quality. We will test this approach
with two different models: (i) we will prompt ChatGPT as detailed in Section
3, and (ii) we will use a high-quality 3.3B parameter model from the NLLB
family of multilingual NMT models (NLLB) proposed in NLLB Team et al. (2022).

##### Translation quality can be significantly improved with external fallback
systems.

Figure 4(a) demonstrates that external fallback systems, particularly NLLB,
can significantly enhance translation quality of originally hallucinated
translations compared to same-family models.181818For reference, in the
English-centric setup, the averaged COMET-22 corpus-level scores for low-,
mid-, and high-resource LPs obtained with M2M(L) are $73.63$, $86.54$, and
$87.19$, respectively. We provide fallback system quality scores for all
language pairs in Appendix E. This improvement is especially notable for low-
resource languages, where both ChatGPT and NLLB consistently boost translation
quality. Remarkably, NLLB generally outperforms ChatGPT as a fallback system
for low- and mid-resource languages, aligning with the findings in Hendy et
al. (2023), which revealed that GPT models have limited capabilities for
lower-resourced languages and lag behind dedicated translation models in those
settings. Nonetheless, ChatGPT still surpasses dedicated M2M translation
systems in these resource levels when used as a fallback system, underscoring
the limitations of relying on same-family models as fallback systems.

##### Oscillatory hallucinations are practically non-existent when ChatGPT is
the fallback system.

From Figure 4(b), we see another benefit of employing external fallback
systems: oscillatory hallucinations are almost entirely eliminated.
Interestingly, consistent with our findings in Section 4, we observe that
ChatGPT produces very few, if any, oscillations, slightly improving the rates
obtained with NLLB. This provides further evidence that, although
hallucinations obtained via prompting LLMs may still occur, they exhibit
different properties and surface forms. Investigating and understanding these
differences in hallucination properties presents an interesting research path
for future work.

##  7 Conclusion

We have comprehensively investigated the phenomenon of hallucinations in
massively multilingual translation models. By departing from the settings
studied in previous work that focused on bilingual models trained on high-
resource language pairs, we were able to explore a wide range of research
scenarios that remained overlooked.

Our analysis revealed several key insights on the prevalence and properties of
hallucinations across various models of different scale, translation
directions, and data conditions, including: the prevalence of hallucinations
across multiple translation directions across different resource levels and
beyond English-centric translation; the emergence of toxicity in
hallucinations; and the effect of scaling up within the same model family on
the prevalence of hallucinations. Additionally, we explored how fallback
systems can mitigate hallucinations and improve overall translation quality.
We found that hallucinations can be sticky and difficult to reverse when using
models that share the same training data and architecture. However, by
leveraging other external models, we can significantly improve translation
performance and virtually eliminate pathologies such as oscillatory
hallucinations.

To support future research on this topic, we are open-sourcing our code and
releasing over a million translations and detection results across several
models and language pairs.

## Limitations

Our study mainly focuses on the M2M family of multilingual models. We chose
this family of models as it includes several models at different sizes and the
largest open-source multilingual NMT model. It is unclear how our findings
generalize to other families of multilingual models (e.g., the NLLB family of
models).

Our detection approaches inherit the limitations that carry over with the
metrics that are leveraged in them. For instance, following all of previous
work, we adopt a BLEU metric to detect hallucinations under perturbation.
However, this and other lexical metrics ranked worst than reference-based
neural metrics in last year’s WMT22 Metrics Shared Task (Freitag et al.,
2022).

We analyzed ChatGPT as it has demonstrated impressive capabilities for
translation and other multilingual tasks, such as MT evaluation.
Unfortunately, the model remains behind API walls and documentation is scarce.
As such, we could not ensure that ChatGPT was not trained on our evaluation
sets, nor could we evaluate the contribution of the source text to ChatGPT’s
translations, which would have enabled detection of detached hallucinations.
Despite these limitations, we believe our findings provide relevant insights
into the properties of translations generated by the model.

## Acknowledgments

We would like to thank Meta AI for open-sourcing the M2M models and
maintaining libraries such as stopes (Andrews et al., 2022) and nllb (NLLB
Team et al., 2022). The work is partially supported by the European Research
Council (ERC StG DeepSPIN 758969), by EU’s Horizon Europe Research and
Innovation Actions (UTTER, contract 101070631), by the FCT through contract
UIDB/50008/2020, and by the projects MAIA and NextGenAI
(LISBOA-01-0247-FEDER-045909 and 2022-C05i0102-02). Part of this work was
performed using HPC resources from GENCI-IDRIS (Grant 2022- AD01101838).

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Supplemental Material

##  Appendix A List of Languages

We summarize the languages used in our work in Table 6.191919We determine the
resource levels following Goyal et al. (2022). We consider very-low resource
languages as low-resource.

ISO | Language | Language grouping | Script | Resource Level
---|---|---|---|---
af | Afrikaans | Germanic | Latin | Low
ar | Arabic | Arabic | Arabic | Medium
ast | Asturian | Romance | Latin | Low
az | Azerbaijani | Turkic | Latin | Low
be | Belarusian | Slavic | Cyrillic | Low
bn | Bengali | Indo-Aryan | Bengali | Medium
bs | Bosnian | Slavic | Latin | Low
ca | Catalan | Romance | Latin | Medium
cs | Czech | Slavic | Latin | Medium
cy | Welsh | Celtic | Latin | Low
de | German | Germanic | Latin | High
el | Greek | Hellenic | Greek | Medium
en | English | Germanic | Latin | High
es | Spanish | Romance | Latin | High
et | Estonian | Uralic | Latin | Medium
fa | Persian | Iranian | Arabic | Medium
fi | Finnish | Uralic | Latin | Medium
fr | French | Romance | Latin | High
gu | Gujarati | Indo-Aryan | Gujarati | Low
ha | Hausa | Afro-Asiatic | Latin | Low
he | Hebrew | Semitic | Hebrew | Medium
hi | Hindi | Indo-Aryan | Devanagari | Medium
hr | Croatian | Slavic | Latin | Low
hu | Hungarian | Uralic | Latin | Medium
hy | Armenian | Armenian | Armenian | Low
id | Indonesian | Malayo-Polynesian | Latin | Medium
it | Italian | Romance | Latin | High
is | Icelandic | Germanic | Latin | Medium
ja | Japanese | Japonic | Kanji; Kana | Medium
kk | Kazakh | Turkic | Cyrillic | Low
km | Central Khmer | Khmer | Khmer | Low
ko | Korean | Koreanic | Hangul | Medium
lt | Lithuanian | Baltic | Latin | Medium
lv | Latvian | Baltic | Latin | Medium
mk | Macedonian | Slavic | Cyrillic | Medium
mr | Marathi | Indo-Aryan | Devanagari | Low
nl | Dutch | Germanic | Latin | Medium
oc | Occitan | Romance | Latin | Low
pl | Polish | Slavic | Latin | Medium
ps | Pashto | Iranian | Arabic | Low
pt | Portuguese | Romance | Latin | High
ro | Romanian | Romance | Latin | Medium
ru | Russian | Slavic | Cyrillic | High
sk | Slovak | Slavic | Latin | Medium
sr | Serbian | Slavic | Cyrillic; Latin | Medium
sv | Swedish | Germanic | Latin | Medium
sw | Swahili | Niger-Congo | Latin | Low
ta | Tamil | Dravidian | Tamil | Low
tl | Tagalog | Malayo-Polynesian | Latin | Low
tr | Turkish | Turkic | Latin | Medium
uk | Ukrainian | Slavic | Cyrillic | Medium
vi | Vietnamese | Vietic | Latin | Medium
zh | Chinese Mandarin | Chinese | Chinese | Medium
zu | Zulu | Niger-Congo | Latin | Low
Table 6: List of languages that we use in our work. We fill the table with the
information available in Fan et al. (2020). For each language, we display the
ISO code, language name, language grouping, script and resource level. The
languages in bold are the bridge languages.

##  Appendix B Dataset Statistics

#####  Flores.

The Flores benchmark (Goyal et al., 2022) consists of a multi-parallel dataset
in 101 languages with sentences extracted from Wikipedia. The sentences in the
dataset are translated from original English sentences. We join the dev and
devtest splits for a total of 2009 parallel sentences for each translation
direction.

##### TICO.

The TICO benchmark (Anastasopoulos et al., 2020) is a multilingual dataset
specialized in the medical domain obtained by combining English open-source
data from various sources, such as scientific articles, government health
announcements, and others. We join the dev and test set for a total of 2100
parallel sentences for each translation direction.

##### WMT benchmarks.

We selected the same English-centric WMT benchmarks that were used as
evaluation sets in the original M2M paper (Fan et al., 2020). Additionally, we
selected recent WMT test sets from the WMT21 and WMT22 campaigns as they were
released after the models were trained. A summary of the datasets used for
each translation direction can be found in Table 7.

cs | de | et | fi | fr | ha | is | ja | lt | lv | ru | tr | uk | zh
---|---|---|---|---|---|---|---|---|---|---|---|---|---
2018 | 2019 | 2018 | 2017 | 2014 | 2021 | 2021 | 2022 | 2019 | 2017 | 2019 | 2017 | 2022 | 2019
Table 7: Specification of the WMT benchmarks used in this work.

##  Appendix C Hallucinations under Perturbation

###  C.1 Data.

##### Perturbations.

We randomly misspell words by changing the characters with a probability of
0.01;202020We use the ButterFingersPerturbation from the NL-Augmenter
framework (Dhole et al., 2022). insert a token chosen randomly from the 50
most frequent tokens or punctuation in the test set; and, randomly title-case
words with a probability of 0.1, guaranteeing that at least one word’s
capitalization is perturbed.

##### Languages.

We pair the following languages with English: af, ar, ast, bn, cs, cy, de, el,
es, fa, fi, fr, he, hi, hr, hu, id, ja, ko, lt, nl, oc, pl, pt, ru, sv, sw,
tl, tr, vi, zh. We selected all bridge languages (see Table 6) and additional
low-resource languages, as they are underrepresented among the bridge
languages. Although Tamil is a bridge language, we did not include it in our
analysis, as we could not find enough candidates with reasonable quality for
hallucinations under perturbation after applying rule (i) of the detection
approach (see Section 4.1).

###  C.2 Supplementary Results

##### Correlation between original translation quality and hallucination under
perturbation.

We present the statistics in Table 8. As mentioned in the main text, the
Pearson correlation between the original translation quality and the detection
outputs of hallucinations under perturbation is very weak across all models.

SMaLL100 | M2M (S) | M2M (M) | M2M (L)
---|---|---|---
$-0.03$ | $-0.05$ | $-0.03$ | $-0.02$
Table 8: Model-based Pearson correlation scores between original spBLEU scores
and hallucination under perturbation assignments across all language pairs.

##### Discussion on hallucinations under perturbation and natural
hallucinations.

As we have mentioned in the main text, hallucinations under perturbation, in
contrast to natural hallucinations, explicitly reveal the lack of robustness
of translation systems. Put simply, hallucinations under perturbation are
cases where the model is not robust and, as a result, undergoes significant
negative shifts in translation quality due to perturbations. This means that
they do not necessarily entail detachment from the source. Hallucinations
under perturbation have been studied as such in previous work, and detection
of these translations with the 2-step method that we adopted in our work (see
Section 4.1) has been consistently used in all of previous research. We
decided to keep the same designations, definitions and detection approach so
as to be consistent with previous work. Nevertheless, we assess here whether
the detected hallucinations under perturbation are, in fact, detached or
oscillatory hallucinations. We found that more than 85% of hallucinations
under perturbation would be detected with our detection approach for natural
hallucinations. Inspection of the non-detected translations revealed that they
usually contain critical mistranslation errors, but do not necessarily exhibit
detachment from the source content (e.g., off-target translations or copies of
the source).

##### Examples of hallucinations under perturbation generated by ChatGPT.

We provide examples of hallucinations under perturbation in Figure 5.

Figure 5: Examples of hallucinations under perturbation generated by ChatGPT.
We present an oracle English translation of the generated hallucination with
Bing Microsoft Translator (Oracle to en). The source perturbations are shown
in italic and coloured differently to the rest of the text.

##  Appendix D Natural Hallucinations

Hereinafter, we will use the terms natural hallucinations and hallucinations
interchangeably.

###  D.1 Translation directions.

##### English-Centric setup.

We pair the following languages with English: ar, ast, az, bn, cs, cy, de, el,
es, fa, fi, fr, he, hi, hr, hu, id, ja, ko, lt, nl, oc, pl, ps, pt, ru, sv,
sw, ta, tr, vi, zh. Similarly to the choice for the experiments on
hallucinations under perturbation, we selected all the bridge languages and
additional low-resource languages.

##### Non-English-Centric setup.

We analyzed the following translation directions: hi-bn, it-fr, de-hu, it-de,
cs-sk, nl-fr, fr-sw, ro-ru, ro-uk, de-hr, hr-sr, be-ru, hr-hu, hr-cs, el-tr,
hr-sk, nl-de, af-zu, ro-hu, hi-mr, ro-tr, uk-ru, ro-hy, ar-fr, ro-de. We
selected language pairs that were used in the analysis from the original M2M
paper on real-world settings for many-to-many translation (Fan et al., 2020).
They represent translation directions that are commonly used in countries that
have official and regional languages that do not include English.

##### Specialized Domains setup.

We pair the following languages with English: ar, fr, hi, id, ps, pt, ru, sw,
zh. We selected languages from the TICO benchmark that are supported by the
M2M models.

###  D.2 English-Centric Translation

####  D.2.1 Evaluation with Flores

##### Evaluation scores.

We present spBLEU and COMET-22 corpus-level scores for the Flores setup in
Table D.2.1.

##### ALTI+ scores.

We present the distribution of ALTI+ scores for all translation directions in
Figure 6. For the M2M models, we find the same trends presented in Ferrando et
al. (2022): smaller source contributions when translating out of English and
for low-resource language pairs. Remarkably, it is also clear from the
distributions that ALTI+ scores for SMaLL100 are not only significantly higher
than those of the M2M models, but they are also more consistent across
different resource levels.

##### Correlation between COMET-22 scores and hallucination rates.

We present the statistics in Table 9. As we have remarked in the main text,
SMaLL100’s reduced hallucination rates for low-resource language pairs do not
necessarily equate to superior translation quality compared to the other M2M
models. We observed a strong correlation between M2M models’ corpus-level
COMET-22 scores and their respective hallucination rates for low-resource
languages, whereas, contrastingly, for SMaLL100 the correlation is weak. This
indicates that despite detaching less from the source content, SMaLL100’s
translations are not necessarily of higher quality to those of other M2M
models. Interestingly, the correlations for the M2M models are significantly
weakened on high-resource language pairs, mirroring observations from previous
work (Lee et al., 2018).

Model | Resource Level
---|---
Low |  | Medium |  | High | All
SMaLL100 | $-0.39$ |  | $-0.14$ |  | $-0.27$ | $-0.52$
M2M (S) | $-0.82$ |  | $-0.67$ |  | $-0.49$ | $-0.81$
M2M (M) | $-0.80$ |  | $-0.68$ |  | $-0.15$ | $-0.79$
M2M (L) | $-0.85$ |  | $-0.38$ |  | — | $-0.83$
Table 9: Model-based average Pearson correlation scores between COMET-22 and
hallucination rates across all language pairs at each resource level.

LP | SMaLL100 |  | M2M (S) |  | M2M (M) |  | M2M (L)
---|---|---|---|---|---|---|---
spBLEU | COMET-22 |  | spBLEU | COMET-22 |  | spBLEU | COMET-22 |  | spBLEU | COMET-22
en-xx directions
1-12[.4pt/2pt] en-ar | 25.96 | 81.01 |  | 25.79 | 81.27 |  | 16.62 | 74.92 |  | 29.74 | 84.43
en-ast | 25.90 | 67.26 |  | 24.79 | 67.80 |  | 32.99 | 69.84 |  | 30.68 | 67.58
en-az | 8.09 | 69.48 |  | 4.15 | 63.38 |  | 7.90 | 71.35 |  | 10.18 | 76.12
en-bn | 27.06 | 80.70 |  | 16.48 | 71.01 |  | 25.53 | 81.78 |  | 28.87 | 82.89
en-cs | 31.02 | 84.02 |  | 30.52 | 83.58 |  | 37.06 | 88.35 |  | 37.79 | 88.74
en-cy | 9.44 | 46.41 |  | 3.27 | 34.13 |  | 14.34 | 56.19 |  | 26.54 | 71.24
en-de | 33.77 | 81.06 |  | 30.99 | 79.40 |  | 39.64 | 85.57 |  | 41.76 | 86.31
en-el | 27.55 | 84.66 |  | 26.70 | 84.19 |  | 32.65 | 87.98 |  | 33.92 | 88.33
en-es | 26.55 | 82.15 |  | 25.36 | 81.07 |  | 29.08 | 84.42 |  | 29.98 | 85.13
en-fa | 27.02 | 80.51 |  | 27.53 | 81.47 |  | 22.69 | 78.56 |  | 29.83 | 84.32
en-fi | 23.93 | 84.31 |  | 22.33 | 83.93 |  | 29.41 | 89.04 |  | 31.59 | 89.82
en-fr | 43.33 | 82.57 |  | 42.30 | 81.99 |  | 49.16 | 86.26 |  | 51.58 | 86.94
en-he | 28.06 | 80.55 |  | 27.09 | 80.79 |  | 30.88 | 83.47 |  | 34.58 | 85.23
en-hi | 32.91 | 74.38 |  | 31.72 | 74.44 |  | 32.61 | 75.79 |  | 36.29 | 77.72
en-hr | 29.03 | 84.92 |  | 27.26 | 84.21 |  | 32.78 | 88.37 |  | 33.65 | 88.76
en-hu | 27.00 | 81.29 |  | 26.35 | 81.32 |  | 32.54 | 86.72 |  | 34.06 | 87.15
en-id | 39.55 | 87.68 |  | 36.45 | 87.05 |  | 42.50 | 89.46 |  | 44.90 | 90.06
en-ja | 22.37 | 84.38 |  | 23.70 | 84.80 |  | 27.61 | 87.61 |  | 28.76 | 88.18
en-ko | 19.36 | 82.89 |  | 19.71 | 83.11 |  | 22.07 | 85.46 |  | 22.99 | 86.20
en-lt | 28.02 | 82.73 |  | 26.03 | 82.54 |  | 32.74 | 87.41 |  | 33.71 | 87.61
en-nl | 27.66 | 82.73 |  | 25.94 | 81.25 |  | 31.01 | 85.51 |  | 32.56 | 86.39
en-oc | 30.40 | 68.86 |  | 23.27 | 67.95 |  | 30.07 | 70.29 |  | 37.25 | 70.39
en-pl | 21.67 | 81.56 |  | 21.18 | 81.59 |  | 25.51 | 86.26 |  | 26.38 | 86.92
en-ps | 6.56 | 58.05 |  | 5.79 | 55.60 |  | 9.26 | 62.55 |  | 13.22 | 68.90
en-pt | 44.45 | 85.92 |  | 43.58 | 85.35 |  | 49.53 | 88.29 |  | 51.54 | 88.77
en-ru | 27.84 | 82.32 |  | 26.94 | 81.83 |  | 33.41 | 86.61 |  | 35.15 | 87.77
en-sv | 40.65 | 86.05 |  | 39.45 | 85.40 |  | 46.37 | 88.95 |  | 47.52 | 89.10
en-sw | 32.22 | 79.73 |  | 21.42 | 72.98 |  | 30.27 | 80.33 |  | 35.21 | 81.67
en-ta | 9.73 | 62.13 |  | 5.45 | 54.28 |  | 5.12 | 56.82 |  | 3.87 | 50.11
en-tr | 28.59 | 83.48 |  | 28.90 | 84.18 |  | 30.63 | 86.49 |  | 33.26 | 87.42
en-vi | 35.87 | 83.74 |  | 34.32 | 82.48 |  | 39.51 | 86.54 |  | 41.03 | 87.02
en-zh | 18.47 | 78.61 |  | 19.12 | 78.73 |  | 23.11 | 83.11 |  | 22.75 | 82.92
xx-en directions
1-12[.4pt/2pt] ar-en | 30.92 | 82.48 |  | 29.92 | 82.14 |  | 29.58 | 80.81 |  | 36.79 | 85.39
ast-en | 34.45 | 77.59 |  | 30.34 | 74.49 |  | 36.78 | 79.80 |  | 38.71 | 80.93
az-en | 15.38 | 75.35 |  | 7.35 | 58.69 |  | 9.19 | 61.38 |  | 9.51 | 61.09
bn-en | 25.96 | 83.54 |  | 25.64 | 83.33 |  | 28.40 | 84.51 |  | 31.43 | 86.03
cs-en | 34.34 | 84.92 |  | 33.78 | 84.45 |  | 38.71 | 86.88 |  | 40.74 | 87.58
cy-en | 19.64 | 57.77 |  | 13.06 | 51.64 |  | 27.86 | 67.19 |  | 30.49 | 69.31
de-en | 38.21 | 85.61 |  | 36.89 | 84.87 |  | 42.85 | 87.77 |  | 44.75 | 88.30
el-en | 30.59 | 84.18 |  | 29.09 | 83.23 |  | 35.39 | 86.45 |  | 37.03 | 87.13
es-en | 27.61 | 83.57 |  | 25.75 | 82.86 |  | 29.54 | 85.22 |  | 31.22 | 85.92
fa-en | 28.97 | 83.18 |  | 28.17 | 82.73 |  | 30.03 | 83.37 |  | 34.28 | 85.99
fi-en | 29.09 | 85.50 |  | 27.95 | 84.49 |  | 33.81 | 88.07 |  | 35.35 | 88.86
fr-en | 39.73 | 85.82 |  | 39.52 | 85.89 |  | 44.31 | 87.86 |  | 45.90 | 88.44
he-en | 33.60 | 82.69 |  | 32.75 | 82.49 |  | 37.90 | 84.99 |  | 40.80 | 86.28
hi-en | 32.05 | 85.25 |  | 30.64 | 84.80 |  | 34.59 | 86.23 |  | 37.23 | 87.49
hr-en | 33.19 | 84.60 |  | 30.98 | 82.72 |  | 37.43 | 86.69 |  | 38.39 | 87.08
hu-en | 30.78 | 84.41 |  | 29.70 | 83.71 |  | 35.35 | 86.88 |  | 36.91 | 87.50
id-en | 36.80 | 85.80 |  | 35.30 | 84.92 |  | 41.02 | 87.56 |  | 43.13 | 88.20
ja-en | 20.33 | 82.40 |  | 20.27 | 82.63 |  | 25.18 | 85.46 |  | 26.25 | 86.11
ko-en | 21.94 | 81.91 |  | 22.28 | 82.26 |  | 26.62 | 85.25 |  | 28.08 | 85.78
lt-en | 28.39 | 82.46 |  | 27.35 | 81.34 |  | 33.36 | 85.19 |  | 34.82 | 85.84
nl-en | 29.63 | 83.69 |  | 28.37 | 82.53 |  | 32.76 | 85.82 |  | 33.83 | 86.28
oc-en | 43.82 | 78.81 |  | 37.74 | 73.82 |  | 46.37 | 79.64 |  | 47.89 | 80.81
pl-en | 26.21 | 81.82 |  | 24.84 | 80.57 |  | 30.38 | 84.39 |  | 31.49 | 84.94
ps-en | 15.72 | 68.46 |  | 12.60 | 64.92 |  | 17.95 | 71.62 |  | 21.22 | 75.34
pt-en | 43.45 | 86.43 |  | 42.62 | 85.94 |  | 48.49 | 88.20 |  | 50.15 | 88.69
ru-en | 29.54 | 82.72 |  | 28.39 | 81.91 |  | 34.30 | 84.87 |  | 35.70 | 85.58
sv-en | 42.62 | 86.78 |  | 42.07 | 86.01 |  | 47.42 | 88.86 |  | 49.34 | 89.41
sw-en | 33.36 | 79.05 |  | 27.74 | 73.22 |  | 35.24 | 80.18 |  | 38.65 | 82.17
ta-en | 16.57 | 71.13 |  | 8.13 | 59.07 |  | 9.83 | 60.60 |  | 13.82 | 66.54
tr-en | 32.04 | 85.21 |  | 31.44 | 85.02 |  | 35.69 | 87.03 |  | 37.70 | 87.82
vi-en | 30.65 | 83.07 |  | 30.23 | 83.00 |  | 35.15 | 85.69 |  | 36.65 | 86.45
zh-en | 22.02 | 81.58 |  | 21.71 | 81.83 |  | 27.09 | 84.76 |  | 27.25 | 84.93

Table 10: Translation quality according to spBLEU and COMET-22 of all the
models and language pairs used in the English-centric Flores setup.

Figure 6: ALTI+ scores for all translation directions for each of the models
considered in the English-centric Flores setup. We represent with horizontal
red lines the model-based specific thresholds tuned on the validation data.

##### Hallucination rates for all language pairs.

We present a heatmap with hallucination rates for each model for all
translation directions in Figure 7.

Figure 7: Heatmap of language-pair specific hallucination rates for each model
in each of the translation directions considered in the English-centric Flores
setup. Pattern-filled cells indicate at least one hallucination for a given
model-LP combination.

####  D.2.2 Evaluation with WMT.

##### Evaluation scores.

We present spBLEU and COMET-22 corpus-level scores in for the WMT setup in
Table D.2.2.

LP | SMaLL100 |  | M2M (S) |  | M2M (M) |  | M2M (L)
---|---|---|---|---|---|---|---
spBLEU | COMET-22 |  | spBLEU | COMET-22 |  | spBLEU | COMET-22 |  | spBLEU | COMET-22
en-xx directions
1-12[.4pt/2pt] en-cs | 23.90 | 81.68 |  | 24.03 | 82.05 |  | 28.61 | 86.85 |  | 30.28 | 87.72
en-de | 35.92 | 77.29 |  | 33.33 | 76.26 |  | 42.85 | 83.10 |  | 44.45 | 84.19
en-et | 25.22 | 82.80 |  | 24.21 | 82.37 |  | 30.50 | 87.66 |  | 31.58 | 88.00
en-fi | 25.52 | 83.83 |  | 25.55 | 84.45 |  | 32.41 | 89.28 |  | 34.60 | 90.02
en-fr | 37.37 | 81.54 |  | 36.54 | 81.52 |  | 41.61 | 85.23 |  | 43.59 | 85.96
en-ha | 8.44 | 59.30 |  | 3.02 | 39.47 |  | 6.94 | 55.06 |  | 9.86 | 61.35
en-is | 19.00 | 69.35 |  | 14.36 | 62.44 |  | 22.63 | 76.00 |  | 25.60 | 77.91
en-ja | 16.25 | 81.69 |  | 16.95 | 81.91 |  | 19.11 | 83.86 |  | 19.94 | 84.86
en-lt | 21.83 | 77.36 |  | 20.93 | 77.76 |  | 25.75 | 83.29 |  | 26.51 | 83.72
en-lv | 17.16 | 73.95 |  | 20.98 | 79.83 |  | 26.49 | 85.10 |  | 28.12 | 85.39
en-ru | 27.01 | 78.71 |  | 26.91 | 78.76 |  | 32.70 | 84.24 |  | 34.79 | 85.50
en-tr | 24.15 | 81.64 |  | 25.00 | 82.86 |  | 26.14 | 84.89 |  | 28.67 | 86.15
en-uk | 24.67 | 79.69 |  | 24.53 | 78.99 |  | 29.02 | 83.11 |  | 29.27 | 83.65
en-zh | 15.50 | 74.39 |  | 16.57 | 74.63 |  | 19.53 | 79.04 |  | 19.40 | 79.34
xx-en directions
1-12[.4pt/2pt] cs-en | 28.08 | 80.64 |  | 28.19 | 80.45 |  | 31.84 | 82.73 |  | 32.78 | 83.38
de-en | 34.35 | 77.89 |  | 35.78 | 77.96 |  | 39.86 | 80.62 |  | 40.91 | 82.10
et-en | 27.92 | 82.14 |  | 27.74 | 81.48 |  | 32.79 | 84.98 |  | 33.48 | 85.43
fi-en | 28.33 | 83.39 |  | 28.08 | 82.54 |  | 33.28 | 86.41 |  | 33.76 | 86.78
fr-en | 34.11 | 83.17 |  | 34.38 | 83.30 |  | 38.56 | 85.52 |  | 40.02 | 86.00
ha-en | 11.25 | 57.35 |  | 7.25 | 48.77 |  | 12.22 | 60.96 |  | 13.77 | 63.71
is-en | 26.09 | 75.40 |  | 23.04 | 70.47 |  | 30.44 | 78.82 |  | 31.62 | 80.83
ja-en | 12.54 | 71.53 |  | 11.98 | 70.84 |  | 14.21 | 73.19 |  | 14.64 | 73.35
lt-en | 29.28 | 81.30 |  | 29.52 | 80.96 |  | 33.74 | 84.42 |  | 34.16 | 84.46
lv-en | 20.14 | 77.19 |  | 21.34 | 77.78 |  | 24.62 | 81.58 |  | 25.12 | 81.94
ru-en | 33.92 | 79.93 |  | 35.15 | 75.36 |  | 39.67 | 82.53 |  | 40.59 | 82.85
tr-en | 24.69 | 80.50 |  | 25.47 | 80.65 |  | 27.54 | 82.41 |  | 29.12 | 82.97
uk-en | 34.09 | 79.40 |  | 32.19 | 77.18 |  | 38.95 | 81.08 |  | 38.82 | 81.12
zh-en | 19.60 | 75.02 |  | 22.38 | 76.39 |  | 27.95 | 79.42 |  | 26.63 | 79.42

Table 11: Translation quality according to spBLEU and COMET-22 of all the
models and language pairs in our analysis of WMT benchmarks.

##### Hallucination rates for all language pairs.

We present aggregated results in Table 12 and a heatmap with hallucination
rates for each model for all translation directions in Figure 8.212121We do
not present results for the high-resource language pairs as these were used as
validation data to set the model-based ALTI+ thresholds. Importantly, the
trends regarding the prevalence of hallucinations analyzed in-depth in Section
5 hold for the WMT language pairs as well.

Model | Low Resource |  | Mid Resource
---|---|---|---
LP Fraction |  Rate (%) |  | LP Fraction |  Rate (%)
SMaLL100 |  2/2 |  $5.658$ |  |  15/20 |  $0.228_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
M2M (S) |  2/2 |  $47.57$ |  |  18/20 |  $0.454_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
M2M (M) |  2/2 |  $27.69$ |  |  13/20 |  $0.157_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
M2M (L) |  2/2 |  $9.364$ |  |  11/20 |  $0.124_{{\color[rgb]{0.5,0.5,0.5}\definecolor[named]{pgfstrokecolor}{rgb}{0.5,0.5,0.5}\pgfsys@color@gray@stroke{0.5}\pgfsys@color<EMAIL_ADDRESS>
Table 12: Fraction of LPs on the WMT English-centric setup for which models
produce at least one hallucination, and average hallucination rate (and
median, in subscript) across all LPs at each resource level. We do not present
the median for the low-resource LPs as we only evaluate on 2 translation
directions. Figure 8: Heatmap of language-pair specific hallucination rates
for each model in each of the translation directions considered in the WMT
English-centric setup. Pattern-filled cells indicate at least one
hallucination for a given model-LP combination.

##### Prevalence of oscillatory hallucinations.

We present a heatmap with the relative prevalence of hallucinations detected
only by TNG (oscillatory hallucinations) in Figure 9.

Figure 9:  Heatmap of the percentage of hallucinations detected with TNG
(oscillatory hallucinations) among all detected hallucinations in the WMT
setup. Pattern-filled cells indicate at least one hallucination detected for a
given model-LP combination.

###  D.3 Beyond English-Centric Translation

##### Evaluation scores.

We present spBLEU and COMET-22 corpus-level scores in Table 13.

##### Hallucination rates for all language pairs.

We present a heatmap with hallucination rates for each model for all
translation directions in Figure 10.

##### Prevalence of oscillatory hallucinations.

We present a heatmap with the relative prevalence of hallucinations detected
only by TNG (oscillatory hallucinations) in Figure 11. The trends follow those
presented in the analysis in the main text (see Section 5.2).

LP | SMaLL100 |  | M2M (S) |  | M2M (M) |  | M2M (L)
---|---|---|---|---|---|---|---
spBLEU | COMET-22 |  | spBLEU | COMET-22 |  | spBLEU | COMET-22 |  | spBLEU | COMET-22
af-zu | 6.89 | 55.73 |  | 4.09 | 43.06 |  | 8.30 | 59.19 |  | 13.83 | 66.00
ar-fr | 28.49 | 77.18 |  | 27.80 | 76.86 |  | 28.45 | 76.60 |  | 34.99 | 82.07
be-ru | 17.40 | 82.46 |  | 15.60 | 78.75 |  | 19.31 | 85.07 |  | 18.34 | 83.72
cs-sk | 32.63 | 90.55 |  | 33.34 | 91.05 |  | 35.90 | 92.19 |  | 36.15 | 92.37
de-hr | 24.69 | 84.98 |  | 23.37 | 84.10 |  | 28.81 | 88.90 |  | 29.52 | 89.18
de-hu | 23.49 | 80.98 |  | 23.26 | 81.30 |  | 28.67 | 86.63 |  | 30.01 | 87.09
el-tr | 19.09 | 79.00 |  | 19.83 | 79.76 |  | 21.66 | 82.35 |  | 23.47 | 83.58
fr-sw | 23.23 | 75.61 |  | 15.62 | 68.06 |  | 21.52 | 74.22 |  | 22.72 | 73.41
hi-bn | 24.12 | 79.42 |  | 16.19 | 71.90 |  | 23.63 | 79.58 |  | 25.63 | 81.06
hi-mr | 14.79 | 65.69 |  | 10.59 | 61.41 |  | 13.68 | 63.22 |  | 18.13 | 68.16
hr-cs | 26.07 | 86.59 |  | 25.87 | 85.82 |  | 30.79 | 90.00 |  | 31.11 | 90.32
hr-hu | 22.22 | 81.22 |  | 21.81 | 80.44 |  | 27.24 | 86.25 |  | 27.93 | 86.44
hr-sk | 27.72 | 86.54 |  | 26.63 | 85.40 |  | 31.84 | 89.75 |  | 31.89 | 90.03
hr-sr | 27.95 | 87.37 |  | 28.34 | 86.75 |  | 30.76 | 89.38 |  | 26.30 | 88.05
it-de | 23.74 | 78.69 |  | 21.89 | 76.74 |  | 27.91 | 83.36 |  | 29.66 | 84.30
it-fr | 32.63 | 82.19 |  | 32.32 | 81.94 |  | 36.74 | 85.39 |  | 37.47 | 85.93
nl-de | 22.64 | 80.23 |  | 21.65 | 78.61 |  | 26.82 | 84.08 |  | 27.97 | 84.84
nl-fr | 27.50 | 77.88 |  | 26.73 | 77.09 |  | 31.41 | 82.03 |  | 33.08 | 82.86
ro-de | 27.89 | 80.45 |  | 25.74 | 78.81 |  | 33.18 | 84.76 |  | 34.73 | 85.53
ro-hu | 23.75 | 80.91 |  | 23.47 | 80.67 |  | 28.95 | 86.30 |  | 29.79 | 86.66
ro-hy | 11.22 | 63.12 |  | 5.06 | 51.47 |  | 6.24 | 51.55 |  | 9.47 | 61.76
ro-ru | 24.61 | 83.48 |  | 23.95 | 82.94 |  | 29.33 | 87.24 |  | 30.99 | 88.06
ro-tr | 23.46 | 81.17 |  | 24.30 | 81.79 |  | 26.16 | 84.49 |  | 27.77 | 85.48
ro-uk | 24.77 | 83.65 |  | 23.93 | 82.96 |  | 28.99 | 87.36 |  | 29.23 | 87.67
uk-ru | 30.01 | 89.44 |  | 29.96 | 89.10 |  | 33.28 | 91.65 |  | 33.56 | 91.56
Table 13: Translation quality according to spBLEU and COMET-22 of all the
models and language pairs used in the non-English-centric setup. Figure 10:
Heatmap of language-pair specific hallucination rates for each model in each
of the translation directions considered in the non-English-centric setup.
Pattern-filled cells indicate at least one hallucination for a given model-LP
combination. Figure 11:  Heatmap of the percentage of hallucinations detected
with TNG (oscillatory hallucinations) among all detected hallucinations in the
non-English-centric setup. Pattern-filled cells indicate at least one
hallucination detected for a given model-LP combination.

###  D.4 Specialized Domains

##### Evaluation scores.

We present spBLEU and COMET-22 corpus-level scores in Table D.4.

LP | SMaLL100 |  | M2M (S) |  | M2M (M) |  | M2M (L)
---|---|---|---|---|---|---|---
spBLEU | COMET-22 |  | spBLEU | COMET-22 |  | spBLEU | COMET-22 |  | spBLEU | COMET-22
en-xx directions
1-12[.4pt/2pt] en-ar | 26.00 | 79.41 |  | 25.21 | 79.50 |  | 16.62 | 74.15 |  | 29.59 | 82.84
en-fr | 37.47 | 77.60 |  | 37.39 | 77.23 |  | 42.81 | 80.11 |  | 44.48 | 80.68
en-hi | 40.49 | 75.45 |  | 38.87 | 75.35 |  | 39.97 | 76.47 |  | 43.02 | 77.99
en-id | 47.64 | 89.00 |  | 44.01 | 88.15 |  | 49.95 | 90.04 |  | 52.33 | 90.55
en-ps | 12.02 | 59.99 |  | 10.74 | 56.89 |  | 16.92 | 65.32 |  | 22.60 | 71.37
en-pt | 48.89 | 86.96 |  | 48.61 | 86.79 |  | 53.72 | 88.87 |  | 55.26 | 89.22
en-ru | 30.43 | 82.34 |  | 29.67 | 81.84 |  | 36.48 | 86.07 |  | 38.52 | 86.75
en-sw | 33.00 | 78.23 |  | 23.98 | 71.91 |  | 31.97 | 78.92 |  | 35.39 | 80.06
en-zh | 19.77 | 80.22 |  | 20.63 | 80.13 |  | 24.74 | 84.18 |  | 23.47 | 83.58
xx-en directions
1-12[.4pt/2pt] ar-en | 31.02 | 81.63 |  | 28.72 | 80.95 |  | 29.38 | 80.26 |  | 37.00 | 84.37
fr-en | 36.68 | 80.89 |  | 36.67 | 80.69 |  | 40.53 | 81.88 |  | 42.09 | 82.24
hi-en | 42.42 | 86.46 |  | 41.24 | 86.06 |  | 45.62 | 87.21 |  | 48.45 | 88.19
id-en | 43.59 | 87.24 |  | 42.09 | 86.38 |  | 48.34 | 88.49 |  | 50.00 | 89.09
ps-en | 24.26 | 73.65 |  | 19.70 | 69.89 |  | 26.95 | 76.76 |  | 31.80 | 80.40
pt-en | 49.06 | 87.48 |  | 48.65 | 87.05 |  | 53.74 | 88.73 |  | 55.43 | 89.03
ru-en | 32.99 | 83.28 |  | 31.05 | 82.74 |  | 36.49 | 84.66 |  | 37.96 | 85.13
sw-en | 34.40 | 78.70 |  | 30.20 | 74.02 |  | 36.67 | 79.61 |  | 39.00 | 81.02
zh-en | 27.77 | 82.25 |  | 27.88 | 82.19 |  | 32.54 | 84.63 |  | 32.86 | 84.82

Table 14: Translation quality according to spBLEU and COMET-22 of all the
models and language pairs used in the specialized domain data setup with the
TICO dataset.

##### Hallucination rates for all language pairs.

We present a heatmap with hallucination rates for each model for all
translation directions in Figure 12.

Figure 12: Heatmap of language-pair specific hallucination rates for each
model in each of the translation directions considered in the specialized
domain setup with TICO data. Pattern-filled cells indicate at least one
hallucination for a given model-LP combination.

##### Prevalence of oscillatory hallucinations.

We present a heatmap with the relative prevalence of hallucinations detected
only by TNG (oscillatory hallucinations) in Figure 13. Similarly to the non-
English-centric setup, the trends follow those presented in the analysis in
the main text (see Section 5.2).

Figure 13:  Heatmap of the percentage of hallucinations detected with TNG
(oscillatory hallucinations) among all hallucinations in the specialized
domain setup with TICO data. Pattern-filled cells indicate at least one
natural hallucination detected for a given model-LP combination.

##  Appendix E Fallback System Analysis

##### English-Centric directions.

We present COMET-22 scores on the original model hallucinated translations for
every translation direction in Figure 14.

Figure 14: Overall translation quality analysis with COMET-22 scores on the
original model hallucinated translations (represented with dashed lines)
across different resource levels for each language pair in the Flores English-
centric setup.

##### Non-English-Centric directions.

We present the results on employing external models as fallback systems in
Figure 15, and COMET-22 scores on the original model hallucinated translations
for each translation direction in the non-English-centric setup in Figure 16.
Importantly, the trends analyzed in-depth in the main text largely hold in
this setup as well. In contrast with the analysis for the English-centric
setup, ChatGPT struggles to outperform all other models in Non-English-Centric
directions, especially in low and mid-resource levels. This is expected: as
ChatGPT was trained on a non-parallel heavily English-centric corpus, it is
likely that it struggles more with translation directions that do not include
English.

(a) Translation quality.

(b) Prevalence of oscillatory hallucinations.

Figure 15: Fallback system analysis for the non-English-centric setup. We
analyse overall translation quality improvements on the original model
hallucinated translations (represented with dashed lines) across different
resource levels via COMET-22 scores in (a), and overall prevalence of
oscillatory hallucinations among the fallback translations in (b).

Figure 16: Overall translation quality analysis with COMET-22 scores on the
original model hallucinated translations (represented with dashed lines)
across different resource levels for each language pair in the non-English-
centric setup.

##  Appendix F Examples of Natural Hallucinations

We provide several examples of English-centric natural hallucinations
generated by the M2M models in Figure 17, which spans 2 pages.

Figure 17: Examples of hallucinations generated by the M2M models in English-
centric directions. We present an oracle English translation of the generated
hallucination with Bing Microsoft Translator (Oracle to en) when necessary.