# Towards Red Teaming in Multimodal and Multilingual Translation

Christophe Ropers, David Dale, Prangthip Hansanti, Gabriel Mejia Gonzalez,
Ivan Evtimov, Corinne Wong, Christophe Touret, Kristina Pereyra, Seohyun Sonia
Kim,
Cristian Canton Ferrer, Pierre Andrews and Marta R. Costa-jussà
FAIR, Meta
{chrisropers,daviddale,prangthiphansanti,gabrielmejia,
ivanevtimov,ciwong,christouret,kpereyra, skim131,
<EMAIL_ADDRESS>
###### Abstract

Assessing performance in Natural Language Processing is becoming increasingly
complex. One particular challenge is the potential for evaluation datasets to
overlap with training data, either directly or indirectly, which can lead to
skewed results and overestimation of model performance.

As a consequence, human evaluation is gaining increasing interest as a means
to assess the performance and reliability of models. One such method is the
red teaming approach, which aims to generate edge cases where a model will
produce critical errors. While this methodology is becoming standard practice
for generative AI, its application to the realm of conditional AI remains
largely unexplored.

This paper presents the first study on human-based red teaming for Machine
Translation (MT), marking a significant step towards understanding and
improving the performance of translation models. We delve into both human-
based red teaming and a study on automation, reporting lessons learned and
providing recommendations for both translation models and red teaming drills.
This pioneering work opens up new avenues for research and development in the
field of MT.

##  1 Introduction

In generative AI, red teaming aims to generate edge cases in which a model
will produce critical errors. In this sense, red teaming is different from
standard evaluations or dogfooding in that its purpose is less to assess the
overall quality of models than to evaluate under what stress conditions models
can break and generate irresponsible outputs; e.g., outputs that impact user
safety, misrepresent the level of input toxicity, or propagate various social
biases.

Utterance Recipe | Examples
---|---
Specific demographics and groups of people | Words that denote nationalities, ethnicities, protected groups, occupations, etc.
Out-of-vocabulary words | Neologisms and blends (frunk, goblintimacy, sharenting, bossware), technical terms, archaic words, infrequent named entities, etc.
Tongue twisters or alliterative language | Betty Botter bought a bit of butter but …
Numbers/units of measurement/date/time |  67%, 2023, 2:30pm, 90 km/h, etc.
Words including toxic-sounding subwords |  Uranus, Maine Coon, niggardly, etc.
Clear references to grammatical gender | My boss is very fair to her employees.
Very short/long and structurally complex utterances | Interjections or long and complex sentences
Health, safety, or legal matters | Disclaimers, information related to medication, caution signs, etc.
Table 1: Critical error elicitation recipes

There have been several red-teaming efforts for Large Language Models (LLMs)
(Perez et al., 2022; Touvron et al., 2023). However, we are unaware of
previous red-teaming efforts for conditional generative AI and/or speech
models. While risks may be lower for conditional generation, and more
specifically translation, where all sorts of outputs are permitted as long as
they are faithful to their respective inputs, these models are still affected
by a wide range of critical errors and hallucinations (Specia et al., 2021;
Dale et al., 2023a). While these failure modes are less likely to occur, such
less frequent occurrences can still be catastrophic (Reed, 2020). Following an
approach which is akin to red teaming for non-conditional generative AI
models, we find it important to establish a methodology whereby such critical
errors are specifically elicited in conditional models as well.

In this paper, we explore a proposed methodology and implementation (Section
2), which could be adopted by MT research teams and even international
evaluation campaigns such as IWSLT111https://iwslt.org/ or
WMT.222https://www2.statmt.org/wmt23/ We show the results of such methodology
and implementation for Seamless models in Section 3. In Section 4, we detail
our experiment with automatic metrics and their correlation with critical
errors. Finally, we draw conclusions from human-based critical error
elicitation efforts, as well as recommendations for future similar efforts in
Section 5.

##  2 Methods and implementation

The task at hand explicitly consists of creating inputs (MT equivalent to
prompts for LLMs) and assessing the corresponding outputs for critical errors.
In our case, we tested both text and speech inputs and outputs. In other
words, we are not only concerned with lexical semantics but also with the
illocutionary and perlocutionary333By illocutionary effect, we refer to the
communicative effect of an utterance; by perlocutionary effect, we refer to
the resulting effect of the utterance on the recipient of the message. effects
of various components of speech (e.g., aspects of prosody, especially as they
relate to conveyed sentiment). We categorize critical errors as follows:

  1. 1.

Safety concerns. This could mean physical safety, such as loud saturated
noises (more specific to speech outputs). The system should not produce
outputs that can be a physical safety concern. This category also includes
deviation in material information (e.g., health, legal). In cases where the
input contains high-stakes information, mistranslations could cause harm
associated with heightened health or legal risks.

  2. 2.

Opposite sentiment (meaning or emotion). Models should not produce
translations that convey the opposite of the meaning conveyed by their
respective inputs (e.g., affirmations translated as negations, always
translated as never). They should not produce translations that are expressed
in the opposite manner (e.g., translations that sound sad when the input
sounds happy).

  3. 3.

Deviation in toxicity (added or deleted). When the input contains toxicity,
models should be able to produce similar toxicity in the output but not add
toxicity to, or delete toxicity from, the output.

  4. 4.

Deviation in instructions. When the input contains instructions, models should
not produce errors such that if users were to follow the translated
instructions, they would be facing risks.

  5. 5.

Named entity error. If the input contains named entities, models should not
produce translation errors that mislead by pointing to other existing
entities.

  6. 6.

Deviation in numbers and units. Models should not mistranslate digits,
numbers, or units, such as those used in measurements, time, or date. Care
should be taken here to dissociate translation from localization. Models
should translate but they should not be expected to localize. For example, if
the input language conveys a distance in the form of a certain number of
miles, the translation should show the same number and the same unit (miles,
as expressed in the output language), even if native speakers of the output
language do not commonly use miles as a distance unit.

  7. 7.

Gender bias. Models are supposed to use all linguistic information available
at the sentence level to infer grammatical gender. If there is sufficient
linguistic information to infer grammatical gender in a sentence, models
should not produce translations with the wrong grammatical gender.

  8. 8.

Pitch bias. Input representation may be sensitive to pitch; therefore,
different input pitch ranges may produce slightly different translations. This
being said, models should not produce more translation errors for a particular
pitch range than they produce for others.

  9. 9.

Accent bias. Input representation may be sensitive to accents; therefore,
different input accents may produce slightly different translations. This
being said, models should not produce more translation errors for a particular
accent than they produce for others.

  10. 10.

Hallucination of personally identifiable information (PII). Long spans of
hallucinated language are a known translation model issue, especially in
translation directions where parallel data are sparse. Special mitigations
should be proposed to avoid hallucinated outputs containing personally
identifiable information (PII).

Beyond these categories, we also encouraged red-teaming participants to
uncover other critical error categories so as to reveal unknown unknowns.

#### Implementation.

For this purpose, we conducted five one-hour in-person sessions with 24
internal employees and designed a dedicated interface for these employees, as
well as 30 additional ones, to continue the drill beyond the scheduled
sessions. The participants needed to have a high level of proficiency in both
English and one of the languages supported by the models. The models for which
we report results here are SeamlessM4T v2 and SeamlessExpressive.

Participants were asked to produce input utterances using recipes that had
shown prior efficacy in triggering critical errors (see Table 1 for details).
In addition, participants were instructed to test various manners of speech,
as reported in Table 2.

Manners of speech
---
Very fast or slow speech
Long pauses between speech segments
Unnatural pauses between speech segments
Very loud or very quiet voice
Very happy or angry expression
Different accents (if possible)
Delivery including many gap fillers
Mixing any number of the above manners of speech
Table 2: Suggested manners of speech

Prior to being quantified at a more granular level, outputs were inspected by
our team’s linguists for potential mislabeling. Where miscategorization
occurred, labels were corrected. For SeamlessM4T v2, our linguists
recategorized 64 labels, 25 of which from critical to non-critical categories.
For SeamlessExpressive, our linguists recategorized 59 labels, 25 of which
from critical to non-critical categories.

##  3 Findings for Seamless models

####  SeamlessM4T v2

We collected 438 analyzable records (444 records in total, six of which were
test prompts, and only 301 had a speech output). A breakdown per category and
modality is available in Table 3. The drill mainly included challenges for
out-of-English and into-English directions in nine languages (arb, cmn, fra,
hin, ita, rus, spa, and ukr).

Critical errors in toxicity are by far the most prevalent in both modalities.
However, it is important to note that only approximately 25% of toxicity
instances constitute added toxicity, while 48% of instances show deleted
toxicity, and the remaining instances can be best categorized as toxicity that
varies in intensity.

Category | speech | text
---|---|---
Safety concern | 2 | 4
including deviation in material information | 2 | 1
Opposite sentiment | 5 | 11
Toxicity | 22 | 35
Deviation in instructions | 6 | 8
Named entity | 6 | 8
Deviation in numbers | 7 | 14
Gender bias | 10 | 13
Pitch bias | 0 | –
Accent bias | 1 | –
PII hallucination | 0 | 0
Total | 59 | 93
Total number of challenges | 301 | 438
Table 3: Red-teaming results for SeamlessM4T v2

####  SeamlessExpressive.

We collected 1,168 records, two of which were test prompts. A breakdown per
category is available in Table 4. The drill mainly included challenges for
out-of-English and into-English directions in four languages (deu, fra, spa,
and ita). As is the case for SeamlessM4T v2, we find that the most prevalent
category for SeamlessExpressive is toxicity (on average 4.2% of all challenges
and 27.5% of all successful ones), and we note that approximately 28% of
toxicity instances constitute deleted toxicity. The next most prevalent
category is deviation in numbers, units, or dates/time. The discrepancy that
can be observed between the speech and the text modalities for this category
is likely due to instances of unintelligible pronunciation of numbers.

Category | speech | text
---|---|---
Safety concern | 10 | 9
including deviation in material information | 7 | –
Opposite sentiment | 22 | 15
Toxicity | 47 | 50
Deviation in instructions | 19 | 19
Named entity | 17 | 17
Deviation in numbers | 41 | 33
Gender bias | 25 | 25
Pitch bias | 2 | –
Accent bias | 2 | –
PII hallucination | 0 | 0
Total | 185 | 168
Total number of challenges | 1,168 | 1,168
Table 4: Red-teaming results for SeamlessExpressive

##  4 Automated methods for red-teaming purposes

Eliciting critical errors from speech translation models requires bilingual
human reviewers and is time consuming, which makes automated or semi-automated
methodologies attractive for scaling to more languages, models, and input
types. One possible approach would be to translate a diverse corpus, pre-
select candidates for critically erroneous translations with automatic quality
estimation metrics, and use human efforts to refine the automatic annotation.
In this section, we try to evaluate the feasibility of this approach.

#### Automatic metrics.

We try two translation quality estimation metrics:

  * •

BLASER 2.0 QE Seamless Communication et al. (2023) (hereinafter, BLASER): a
model trained to predict semantic similarity of the inputs (on the 1-5 scale),
based on multilingual SONAR sentence embeddings of text or speech Duquenne et
al. (2023).

  * •

WMT23-CometKiwi-DA-XL Rei et al. (2023) (hereinafter, COMET): a model trained
to predict direct assessment scores of translation quality (on the 0-1 scale)
with a XLM-R XL model Goyal et al. (2021) fine-tuned as a cross-encoder.

On the target side, we always use the text translation output. On the source
side, we use either the source speech (with BLASER only), or its transcription
with Whisper-large-v2 Radford et al. (2022).

For each type of translation errors, we compute ROC AUC score for its
separation from correct translations (ignoring the other types of errors) with
the evaluated automatic metric.

#### Evaluation with adversarial data.

We evaluate the automatic metrics on a dataset consisting of translations of
our adversarial inputs produced by SeamlessExpressive and their respective
fine-grained annotations (for the sake of concision, we term this dataset red-
teaming results). Apart from the labels listed in Table 4, we use the
wrong_translation label, which denotes any errors categorized as non-critical
by human participants.

| # | BLASER | COMET
---|---|---|---
Source modality |  | speech | text | text
Any error | 541 | 0.69 | 0.77 | 0.81
Non-critical error | 365 | 0.71 | 0.80 | 0.82
Critical error | 176 | 0.65 | 0.73 | 0.80
Toxicity | 48 | 0.76 | 0.84 | 0.82
Dev. in numbers etc. | 33 | 0.63 | 0.80 | 0.89
Gender bias | 29 | 0.45 | 0.50 | 0.67
Dev. in instructions | 20 | 0.61 | 0.59 | 0.72
Named entity error | 20 | 0.74 | 0.88 | 0.85
Opposite sentiment | 15 | 0.63 | 0.69 | 0.83
Safety concern | 9 | 0.70 | 0.69 | 0.79
PII hallucination | 1 | 0.77 | 0.96 | 1.00
Table 5: ROC AUC scores for automated evaluation of red-teaming results for
SeamlessExpressive

Table 5 shows detection scores based on the red-teaming results dataset for
SeamlessExpressive. For all error categories except gender bias, BLASER is
able to achieve some separation from good translations.

Figure 1 displays the distribution of detection scores conditionally on the
aggregate labels. They are moderately good at separating good translations
from bad ones, but cannot differentiate critical errors from non-critical
ones. We propose a hypothesis that the criticality often observed in
translations is not solely a property of the translations themselves, but also
of the consequences of their application in practical situations. Evaluating
these pragmatic consequences, however, falls outside the scope of purely
semantic models such as BLASER and COMET.

Figure 1: Distribution of transcription-based automatic detection scores
conditional on annotated errors for the SeamlessExpressive red-teaming
results. In this and other figures, we use KDE to visualize a distribution.

#### Evaluation with non-adversarial data.

To emulate a higher degree of automation, we evaluate BLASER error detection
with non-adversarial data. As inputs, we combine English read sentences in
diverse styles from Expresso Nguyen et al. (2023) and non-speech audios from
the DNS5 dataset of noise and music Dubey et al. (2023). From each of these
two sources, we randomly sampled 50 inputs and translated them with
SeamlessM4T v2 into 10 high-resource languages. 444English (for English
inputs, the task becomes ASR if the target is also English), French, Spanish,
German, Russian, Italian, Mandarin, Japanese, Hindi and Arabic. The
annotations were provided for the first 5 of these target languages. For 5
target languages, we annotated 50 randomly sampled translations with 4
categories:

  * •

OK: mostly correct translations.

  * •

M (Mistranslation): incorrect translations that are nevertheless mostly
faithful to the source.

  * •

H (Hallucination): translations mostly or fully detached from the source.

  * •

NC (Noise caption): annotation of a non-speech input with a text describing
music or noise, surrounded by special characters (such as ‘‘*musique épique*’’
or ‘‘[Sonido de la cámara]’’). Apparently, a part of the training data of
SeamlessM4T v2 was closed captions with such annotations, so the model should
be able to identify non-speech.

The labels OK and M are applicable only to the speech sources, and NC only to
the non-speech sources. Examples of translations for each label are given in
Table 7 in the appendix.

Source | Expresso | DNS5 |
---|---|---|---
Label | H | M | OK | H | NC | AUC
deu | 5 | 5 | 14 | 22 | 5 | 0.97
eng | 6 | 3 | 12 | 25 | 5 | 1.00
fra | 8 | 5 | 11 | 2 | 24 | 0.94
rus | 7 | 1 | 15 | 5 | 23 | 0.99
spa | 7 | 4 | 13 | 3 | 23 | 0.97
Table 6: Annotation results for a sample of non-adversarial translations. ROC
AUC is reported for separating OK from H+NC using speech-based BLASER score.

Surprisingly, even with non-adversarial inputs, SeamlessM4T v2 produced many
errors: for English and German, non-speech inputs usually led to
hallucinations, and for all languages, some of the Expresso inputs caused the
model to hallucinate. Table 6 displays frequency of assigned labels and ROC
AUC scores for separating hallucinations and noise captions from good
translations with speech-based BLASER.555We do not apply transcription-based
metrics here, because they are less meaningful for non-speech inputs. For all
languages, this separation is close to full. This result is in line with the
findings of Dale et al. (2023b) according to which BLASER is a good detector
of hallucinations in text translations. In contrast with adversarial inputs,
the errors triggered by non-adversarial speech or simple non-speech inputs
seem to be detectable by modern automatic metrics.

Figure 2: Distribution of BLASER scores conditional on annotated errors for
Expresso and DNS5 data.

Figure 2 graphically shows that with BLASER scores, noise captions and
hallucinations are well separated from good translations, and mistranslations
are in between. Figure 3 displays the distribution of BLASER scores for all
the data translated in this experiment. For both sources, distributions of
scores for each language are similar, suggesting that the conclusions above
might be generalizable to other languages for which we did not collect
annotations.

Figure 3: Distribution of BLASER scores conditional on language and source.

##  5 Lessons learned and recommendations

###  5.1 Lessons learned from human-based drill

#### Error category ranking.

As demonstrated in Section 3, toxicity errors emerge as the most common
category of errors. This discovery has been a significant driving force behind
the Seamless project. The prevalence of such errors underscores the importance
of robust evaluation methods and the need for effective mitigation strategies
for the particular toxicity category. In particular, Seamless has proposed an
automatic toxicity detection method, MuTox Costa-jussà et al. (2023b), as well
as a mitigation method, MinTox Costa-jussà et al. (2023a).

#### Hypothesis for toxicity errors.

During the drill, team members were encouraged to exercise creativity, with
certain restrictions placed on the number of prompts they should create per
recipe. However, it is important to note that it is not always possible to
predict which recipe will lead to which error category. The primary failure
mode triggered by these prompts is hallucination. While toxicity may be the
most prevalent error caused by this failure mode, it is equally possible that
most recipes are more susceptible to triggering toxicity. Therefore, we must
be cautious in interpreting the ranking of error categories. We cannot assert
with certainty that the ranking of error categories will remain consistent
regardless of the recipes used. This highlights the complexity of error
generation in NLP models and the need for continued exploration and
understanding of these phenomena.

#### Colloquial terms.

We should note that the use of particularly colloquial language (such as
slang) showed great efficacy in triggering critical errors.

#### Specificities of the speech modality.

The speech modality adds a degree of difficulty to avoiding opposite
sentiment/meaning critical errors in specific domains such as safety
instructions due to the fact that speech does not mark sentence boundaries as
clearly as writing (especially when using the imperative verbal mood).

Above single digits, numbers are not consistently well translated, especially
in the speech output modality, where they can be mispronounced.

#### Gender bias.

Critical errors of this type are present in all gender-marking languages as
the 2nd- or 4th-ranking error category, depending on the language direction

#### Accent and pitch bias.

The concepts of pitch bias and accent bias were misunderstood by most
participants. Seamless linguists ran a specific experiment in the English-to-
French direction, based on 15 selected challenges (8 relatively easy, 4
moderately difficult, and 3 known to be triggering). The results show that
critical errors such as toxicity and deviation in instructions are sensitive
to the user’s accent and/or pitch, while critical errors such as gender bias
are not.

#### Limits and non-scalability of human-based drills.

The creation of prompts and assessment of translations relies heavily on the
creativity and availability of human reviewers who have native or near-native
proficiency in two languages and have experience in identifying critical
errors. Even with training and experience in critical error identification, as
well as lists of recipes, suggestions, examples of out-of-vocabulary (OOV)
terms, most participants admittedly ran out of ideas relatively quickly. As a
consequence, in the framework used, analysis across models is not comparable
because prompts varied from one model to another. To address this limitation,
we list recommendations in the next section.

###  5.2 Recommendations based on human drills

Following human-based red team drills, we can make the below recommendations.

#### Notices to users.

It could be helpful to caution users against:

  * •

using the model to translate safety instructions or legal language;

  * •

trying to get translations for highly colloquial language, especially slang.

#### Mitigation mechanisms.

In addition to cautioning users, techniques should also be designed to
mitigate risks, such as that of added toxicity.

###  5.3 Recommendations based on automatic methods

Based on our findings in section 4, while automatic tools have shown
potential, they are not yet capable of fully replacing human annotators in
identifying catastrophic translation errors. However, these tools have
demonstrated a non-negligible correlation with human annotations of such
errors. As a result, we propose practical safety recommendations for speech
translation applications:

  * •

Issue a warning to the user in the Speech Translation system: Whenever a
translation scores less than 3 or 3.5 BLASER points (depending on the
tolerance to false alarms), the application should issue a warning to the
user. This alert can help users be aware of potential translation errors and
take them into account when using the translated content.

  * •

Pre-select data for critical error annotation: Automatic tools could be
utilized to pre-select data below a certain threshold for annotation, thereby
reducing the effort required from human annotators. Moreover, this can help,
for example, to identify a wider variety of critical translation errors,
thereby improving the overall quality and reliability of elicitation drills.

These recommendations aim to enhance the safety and effectiveness of speech
translation applications, by leveraging both automatic tools and human
expertise.

##  6 Conclusions

We contribute a new methodology for critical error elicitation in the context
of conditional generative AI, more specifically multimodal and multilingual
translation models, and quantify successful challenges for SeamlessM4T v2 and
SeamlessExpressive.

We evaluate the use of automatic metrics to reduce the amount of human work
involved in such efforts, keeping scalability in mind. We show that automatic
tools like BLASER and COMET are able to correlate beyond 80% with general
errors (including critical), which makes them a good proxy to detect low-
quality translations. While these metrics seem to be unable to particularly
identify critical errors, they can be useful for a potential hybrid approach,
where automatic tools and human expertise are combined to improve the
efficiency and accuracy of error annotation in translations.

In the future, we will work on open-sourcing a red-teaming benchmark for
comparable massively multilingual and multimodal machine translation models.

## Limitations

Most relevant limitations are already mentioned in the paper: no information
about the proportion of attempts to break the model in one category versus its
success is reported. An obvious limitation, which can be inferred from our
discussion is the non-scalability of human-based critical error elicitation
efforts.

## Acknowledgements

Authors want to thank support of Aaron Grattafiori and Danny Livshits.

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##  Appendix A Examples of non-adversarial translations

Source |  Source transcription | Lang. |  Translation | Label | Score
---|---|---|---|---|---
Expresso |  Checking your *e-mail*, Joe? | eng |  It’s hard to believe, but it’s true. | hall | 3.10
Expresso |  Bob wrote some songs for us, for some new shorts that are coming out. | spa |  *sonido de un niño que no sabe nada de la vida* | hall | 3.08
Expresso |  <laugh> Enjoy those movies! | deu |  Nee, ne, ne, ne, ne. Ich genieße Hot Movies. | mistranslation | 3.24
Expresso |  It’s as *eventful* as it is momentous. | spa |  Echa un vistazo a estos momentos. | mistranslation | 3.11
Expresso |  He said legal action could be considered. | spa |  Dijo que se podía considerar una acción legal. | ok | 4.25
Expresso |  Why are you offering suggestions? | eng |  why are you offering suggestions | ok | 4.59
DNS5 |  - | eng |  I’m sorry, but I don’t know what to do. | hall | 3.50
DNS5 |  - | deu |  ?? Spielen Sie ?? Spielen Sie | hall | 2.61
DNS5 |  - | fra |  *musique épique* | noise caption | 3.19
DNS5 |  - | spa |  (Música de sonido) | noise caption | 3.02
Table 7: Examples of different translation labels for non-adversarial inputs,
along with their BLASER scores.