# CultureLLM: Incorporating Cultural Differences into Large Language Models

Cheng Li1,2111Work done during Cheng’s internship at MSRA. Contact:
<EMAIL_ADDRESS>Mengzhuo Chen2, Jindong Wang1222Corresponding
author: Jindong Wang (jindong.wang@microsoft.com)., Sunayana Sitaram1, Xing
Xie1
1Microsoft Research 2Institute of Software, CAS

###### Abstract

Large language models (LLMs) are reported to be partial to certain cultures
owing to the training data dominance from the English corpora. Since
multilingual cultural data are often expensive to collect, existing efforts
handle this by prompt engineering or culture-specific pre-training. However,
they might overlook the knowledge deficiency of low-resource culture and
require extensive computing resources. In this paper, we propose CultureLLM, a
cost-effective solution to incorporate cultural differences into LLMs.
CultureLLM adopts World Value Survey (WVS) as seed data and generates
semantically equivalent training data via the proposed semantic data
augmentation. Using only $50$ seed samples from WVS with augmented data, we
fine-tune culture-specific LLMs and one unified model (CultureLLM-One) for $9$
cultures covering rich and low-resource languages. Extensive experiments on
$60$ culture-related datasets demonstrate that CultureLLM significantly
outperforms various counterparts such as GPT-3.5 (by $8.1$%) and Gemini Pro
(by $9.5$%) with comparable performance to GPT-4 or even better. Our human
study shows that the generated samples are semantically equivalent to the
original samples, providing an effective solution for LLMs augmentation.

##  1 Introduction

Culture is a complex construct that encodes various identities, including but
not limited to, language, nationality, region, religion, and gender identity.
Cultural bias is widely present around the world, which refers to the tendency
to favor specific cultural perspectives, values, and norms that lead to
subjective opinions and can offend people of other cultures. For example,
according to the World Value Survey (wvs), Arabic culture believes that men
are better political leaders than women, while people in the United States
disagree.333We respect all opinions in different cultures. As large language
models (LLMs) (openai2023gpt4; gemini) become prevalent, they are reported to
suffer cultural bias and are specifically partial to Western culture, as
English corpora dominate training data (liu2023multilingual; cao2023assessing;
masoud2023cultural; naous2023having; wang2023not; johnsonghost). Low-resource
cultures exist widely due to the lack of training data from other cultures.
LLMs’ cultural bias presents a major bottleneck in human-AI collaboration and
significantly hinders AI democracy.

Figure 1: Overview of CultureLLM. CultureLLM consists of three steps:
sampling, semantic data augmentation, and fine-tuning. Both culture-specific
and unified CultureLLM can be fine-tuned.

Tackling cultural bias requires an LLM to embrace cultural differences
(hofstede1984culture). kovavc2023large and wang2023not thought LLMs have
enough knowledge of all cultures and devised prompt engineering technologies
to induce LLMs to exhibit specific cultural perspectives. However, they are
not effective, especially in low-resource cultures with limited data. Another
line of work pre-trained culturally-aware LLMs and then fine-tuned on specific
datasets (chan2023harmonizing; nguyen2023seallms; pipatanakul2023typhoon;
abbasi2023persianllama; lin2023taiwan). They require the collection of large-
scale pre-training and fine-tuning datasets and extensive computing resources,
thus are not affordable to ordinary researchers and cannot handle low-resource
culture. To date, training culturally-aware LLMs at affordable costs remains a
challenge.

In this paper, we propose CultureLLM, a cost-effective444Fine-tuning a
CultureLLM only costs $6 via OpenAI API. solution to incorporate cultural
differences into LLMs. To be specific, we focus on cultural values in this
work. As shown in Figure 1, CultureLLM consists of three steps: sampling,
semantic data augmentation, and fine-tuning. Inspired by Attitude-Behavior
Consistency theory (fazio1981direct) which emphasizes that people’s opinion is
consistent with their behaviors, we use the World Values Survey (WVS) (wvs) as
seed data. Then, we devise a semantic data augmentation approach to generate
semantically equivalent samples. The aim is to generate semantic equivalent
inputs, thus we can get the ground-truth from seed data directly. Finally,
CultureLLM is obtained by fine-tuning on both the seed and the generated data.
WVS is a public opinion poll that contains people’s opinions on cultural
topics from different countries. To be specific, we select $50$ seed samples
from WVS, covering $7$ topics: “social values", “migration", “security",
“science and technology", “religious values", “ethical values and norms", and
“political interest and political participation". Using these generated
samples and answers from people in different cultures, we fine-tune specific
and unified LLMs: specific LLMs are tailored for each culture such as
CultureLLM-Ar for Arabic and CultureLLM-Tr for Turkish; unified LLMs
(CultureLLM-One) are one LLM that fits all cultures.555The bound of culture is
unclear, and we use the main spoken language to distinguish cultures in this
work (delanoy_2020).

We build $9$ specific CultureLLM and a CultureLLM-One covering both high- and
low-resource cultures: Arabic culture, Bengali culture, Chinese culture,
English culture, German culture, Korean culture, Portuguese culture, Spanish
culture, and Turkish culture. Then we evaluated them on $8$ culture-related
downstream tasks: offensive language detection, hate speech detection, stance
detection, toxicity detection, threat detection, bias detection, abusive
detection, spam detection, and an open-ended generative task. We have $60$
test sets, which contain $68,672$ test samples in total. Experiments show that
CultureLLM fine-tuned on GPT-3.5 significantly outperforms GPT-3.5 by $8.1$%
and outperforms Gemini pro (gemini) by $9.5$% on average F1 score, achieving
comparable or even better performance with GPT-4. Our human study of $50$
people demonstrates that the augmentation method can generate semantically
equivalent samples. We further interpret the rationale behind its
effectiveness by exploring the fine-tuning data size and case studies.
Finally, results on Big-Bench Hard (suzgun2022challenging) and GSM8K
(cobbe2021training) indicate that CultureLLM is resistant to catastrophic
forgetting. CultureLLM also supports open-source fine-tuning.

Our contributions are three-fold:

  1. 1.

We presented CultureLLM, a cost-effective fine-tuning solution to build
culturally-aware LLMs.

  2. 2.

We proposed semantic data augmentation, an augmentation approach to generate
high-quality and diverse training data for LLMs.

  3. 3.

We conducted extensive experiments across a wide range of cultures and LLMs,
showing that LLMs performs consistently well in all downstream tasks.

##  2 Related Work

###  2.1 Cultural Problem and Solution in LLMs

Previous efforts have shown that LLMs exhibit the same cultural problems as in
human society. niszczota2023large proved that GPT-4 can replicate the cross-
cultural differences for each personality factor through large-scale
experiments. Meanwhile, other works also found that LLMs can reflect cultural
bias and dominance in human society (liu2023multilingual; cao2023assessing;
masoud2023cultural; naous2023having; wang2023not; johnsonghost), e.g., Western
culture dominance, since the major training corpus such as Pile (gao2020pile)
is in English.

The ideal solution is to enhance the cultural awareness of LLMs. There are
mainly two types of approach: prompt engineering and pre-training.
kovavc2023large; wang2023not thought LLMs as superpositions of cultural
perspectives, which can be prompted to targeted cultural perspectives. while
rao2023ethical encoded cultural values in the prompts. Although PE is cheap,
its effectiveness is challenged, especially in low-resource cultures where
LLMs lack such cultural knowledge due to lack of representation in pre-
training data. Another line of research is pre-training and fine-tuning
(chan2023harmonizing; nguyen2023seallms; pipatanakul2023typhoon;
abbasi2023persianllama; lin2023taiwan) that trains culturally-aware LLMs for
different cultures by collecting large-scale pre-training datasets and then
performed fine-tuning for better alignment. While they achieved great
performance, this approach is too expensive and time-consuming, thus it is
difficult to apply to more cultures and countries. They still suffer from a
low-resource culture problem where the pre-training data are difficult to
collect. MaLA-500 (lin2024mala500) trained a new LLM on Llama 2 to cover $534$
languages, which is resource intensive.

###  2.2 Data Augmentation for LLMs

Human-annotated data are high-quality but expensive. Due to the strong
generation ability of LLMs, many works focused on data augmentation leveraging
LLMs. yu2023metamath; liu2023tinygsm used LLMs to augment the math data and
then fine-tuned with those data. li2023self synthesized data with two devised
modules: self-augmentation and self-curation. chen2024self introduced a self-
play mechanism, where LLM generates its own training data from its previous
iterations, refining its policy by discerning these self-generated responses
from those obtained from human-annotated data. There are also other uses for
synthetic data, such as knowledge distillation (wang2023let) and improving
text embedding tasks (wang2023improving). Our data augmentation approach also
adopts LLMs for data generation, but we add controllable modules such as
template editing, synonym replacement, and semantic filter to ensure the
diversity and semantic equivalence of the generated samples. Our approach can
also be used as a general augmentation method in other applications.

###  2.3 Culture-related Research and Value Alignment

Efforts in cultural datasets (nguyen2023extracting; fung2022normsage) focus on
cultural common sense and norms, respectively. However, they generate data
from only the English or Chinese corpus and thus may contain cultural bias
toward other cultures. In contrast, World Values Survey (WVS) (wvs) is a
large-scale pool that contains answers from vast people of different cultures,
thus providing more objective cultural values from specific cultures.

This work is also related to value alignment (ji2023ai; shen2023large;
yao2023instructions), which focuses on aligning the values of LLMs with
human’s by designing algorithms for value measurement and behavior alignment.
In contrast, this work primarily emphasizes value understanding with the
potential to be extended for value alignment. For instance, semantic
augmentation can be used to generate training data for alignment-related
tasks.

##  3 CultureLLM

In this section, we introduce how to build CultureLLM by fine-tuning existing
LLMs via semantic data augmentation.

###  3.1 Overview

Cultural differences are prevalent in various cultures and backgrounds,
leading to an impact on outcomes in downstream applications such as hate
speech and biased language. To address the gap between low-source cultural
data collection and its wide applications, we design CultureLLM by fine-tuning
an LLM on data generated by our novel semantic data augmentation approach.
Figure 1 presents an overview of CultureLLM, where the first step is to sample
a subset of data from an existing World Value Survey (WVS) (wvs) that
represents different opinions (answers) towards the same value questions given
by native users. The adoption of WVS is inspired by Attitude-Behavior
Consistency theory (fazio1981direct), which emphasizes the strong relationship
between attitude and behavior. Therefore, WVS serves as an ideal seed for data
augmentation.666WVS is only one feasible option for seed and other surveys can
also be used. But, as we discussed, the cultural survey data is extremely rare
and WVS could be the most comprehensive one. After sampling, the second step
is to generate augmented data using our proposed semantic augmentation
approach (Section 3.3) and then fine-tune a CultureLLM for each specific
culture such as CultureLLM-Ar for Arabic culture and CultureLLM-Tr for Turkish
culture.

Generally speaking, we use $\mathcal{D}_{d}=\\{(x_{j},y_{j}^{d})\\}_{j=1}^{n}$
to denote the seed and
$\mathcal{D}^{\prime}_{d}=g(\mathcal{D}_{d})=\\{(x^{\prime}_{j},y_{j}^{d})\\}_{j=1}^{n^{\prime}}$
as the augmented data with $g(\cdot)$ the augmentation algorithm. Note that
the question $x$ here is the _same_ in all cultures in WVS and $d$ is the
cultural index denoting _different_ answers to the same question $x$. For
example, for a question $x$=“Do you agree with on the whole, men make better
political leaders than women do?”, the answer $y=\text{Disagree}$ if
$d=\text{English}$; and $y=\text{Strongly agree}$ if $d=\text{Arabic}$.
Therefore, we only augment the question $x$ to be $x^{\prime}$ but retain the
same opinion $y$ as the original $x$. We further denote vanilla LLM and
CultureLLM as $f$ and $f^{\star}$, respectively. Then, denoting $\ell$ as the
loss function, our learning objective is formulated as:

$f^{\star}=\arg\min_{f}\mathbb{E}_{\begin{subarray}{c}(x_{j},y_{j}^{d})\in\mathcal{D}_{d}\\\
(x^{\prime}_{j},y_{j}^{d})\in
g(\mathcal{D}_{d})\end{subarray}}[\ell(f(x_{j}),y_{j}^{d})+\ell(f(x^{\prime}_{j}),y_{j}^{d})].$

Figure 2: Details of semantic data augmentation. First, semantic templates are
generated via rephrasing, semantic filtering, and sentence parsing. Then,
training samples are generated by context-aware synonyms replacement and
semantic filtering.

###  3.2 Sampling

The sampling process should follow two principles: 1) cover as many cultural
topics as possible and 2) sample questions that can be clearly answered by
LLMs. Based on the two principles, we manually select $n=50$ questions and
rewrite them into the Question-Answer (QA) format, covering $7$ topics, namely
social values, security, science and technology, religious values, ethical
values and norms, political interest and political participation, and
migration.

###  3.3 Semantic Data Augmentation

The samples from WVS are not enough to fine-tune, which are then augmented by
our semantic data augmentation approach. In a formal sense, semantic
augmentation retains the original ground-truth opinions ($y_{d}$) from
different cultures and only generates semantically equivalent questions ($x$).
A naive augmentation approach is to directly use strong LLMs such as GPT-4 to
generate new samples (walters2023fabrication), which could introduce mode
collapse, as generation quality can only be controlled by prompts.
Furthermore, since LLMs could suffer from cultural bias, directly generating
cultural data using prompts could lead to unexpected or even erroneous
outputs.

As shown in Figure 2, the augmentation consists of two stages: semantic
template generation and intact sentence generation. The first stage generates
several semantically equivalent but stylistically different sentences and
parses them into semantic templates. The second stage then generates samples
by replacing certain words in the semantic templates. Such an augmentation
introduces more diversities: The first stage introduces sentence-level
diversities, and the second one introduces word-level diversities.

####  3.3.1 Semantic Template Generation

This stage generates semantically equivalent question templates
$\mathcal{T}=\\{t_{i}\\}_{i=1}^{k}$ based on $x\in\mathcal{D}_{d}$. The
generation process is nontrivial since there are two challenges ahead: 1) the
naturalness and diversities and 2) the semantic preservation. We solve the
first challenge by leveraging GPT-4 as the generator with certain prompts to
ensure naturalness and diversity. Then, we solve the second challenge by
introducing a semantic preservation filter $\mathcal{P}$ to measure the
similarity between the original and generated sentences.777For computational
efficiency, we use BERT embedding as $\mathcal{P}$ while other models can also
be used.

Specifically, we first use the prompt “Could you please generate [$n$]
sentences that (1) have different sentence structures and (2) have the same
meaning with the following sentence: $x_{i}$” to generate $n$ sentences using
GPT-4. Then, we denote the embedding of the original sentence and the
generated sentences as $z=\mathcal{P}(x)$ and $z^{\prime}=\mathcal{P}(t)$,
respectively. Then we compute their similarity score $c=\cos(z,z^{,})$. If $c$
passes the threshold value $\tau$, the generated sentence will be reserved:

$\mathcal{T}=\\{t_{i}|\cos(\mathcal{P}(t_{i}),\mathcal{P}(x_{j}))>\tau\\},\forall
x_{j}\in\mathcal{D}_{d}.$

Specifically, for sample “Do you agree with One of my main goals in life has
been to make my parents proud?”, we generate $m$ samples using GPT-4, which
are then go through the semantic filter $\mathcal{P}$ to eventually retain
$k(k\leq m)$ semantically equivalent sentences, e.g. “Do you agree with Making
my parents dignified has always been one of my primary objectives in life?"
and “Do you agree with The goal to bring vanity to my parents has been a
central life goal of mine?"

To make the generated data more diverse, we then parse the $n$ sentences to
find the proper components to replace, which construct the templates. For
efficiency and cost-saving, we use NLTK (loper2002nltk) to find replaceable
words, such as adjectives, adverbs, nouns and verbs. The semantic templates
are like “Do you agree with The [x] to bring [x] to my parents has been a [x]
life [x] of mine?" where “[x]” is the replaceable part. In total, we generate
$k$ templates for each sample $x_{j}\in\mathcal{D}_{d}$.

####  3.3.2 Intact Sample Generation

This step is to randomly replace synonyms in templates to generate fine-tuning
samples. Synonyms are diverse in different contexts, and existing synonym
tables could ignore their contexts, thus we apply GPT-4 to generate context-
aware synonyms for words in the templates and randomly replace some of them.
To further preserve semantics, we also use the semantic preservation filter in
this step. After filtering, we generate $m$ samples for each template
$t_{i}\in\mathcal{T}$, and get $n^{\prime}=mnk$ samples for all
$x_{j}\in\mathcal{D}_{d}$ in total.

For instance, the intact samples for template “Do you agree with The [x] to
bring [x] to my parents has been a [x] life [x] of mine?" could be “Do you
agree with The goal to bring pride to my parents has been a central life goal
of mine?" and “Do you agree with The hope to bring vanity to my parents has
been a central life goal of mine?"

Our human study in Section 4.6 shows that semantic data augmentation can
generate high-quality and semantically equivalent sentences.

###  3.4 Converting WVS opinions to groundtruth

Since culture is a complex construct, we use languages spoken by geographical
cultures (represented by countries) in WVS to represent broader cultures and
arrive at a set of $9$ cultures in total. In cases where a language is spoken
by more than one geographical culture, we pick representative countries and
use the average of all answers as groundtruth. Our final set of cultures
represented as described above is Arabic (for which we select Jordan and
Iraq), Spanish (for which we select Mexico and Argentina), Bengali, Chinese,
English, German, Korean, Portuguese, and Turkish.

###  3.5 Fine-tuning

Finally, CultureLLM is obtained by fine-tuning an LLM on the combination of
the seed and the generated data. Specifically, we fine-tune two types of
CultureLLM: 1) culture-specific LLMs for each language such as CultureLLM-Ar
and CultureLLM-Bn, and 2) one unified LLM for all languages, denoted as
CultureLLM-One. Culture-specific LLMs are tailored for one specific culture by
setting $d$, namely, $\\{\mathcal{D}_{d},\mathcal{D}^{\prime}_{d}\\}$. On the
other hand, CultureLLM-One is trained on all datasets:
$\\{\mathcal{D}_{d},\mathcal{D}^{\prime}_{d}\\}_{d\in\text{all language}}$ to
serve as a unified LLM for all cultures. Note that since all languages have
the same input question $x$ but different answers $y$, we need to manually
write different prompts in the instruction to distinguish them. For example,
we add “You are an Arabic chatbot that know Arabic very well " before Arabic
samples. CultureLLM can be used in culture-related downstream applications. In
the following, we use CultureLLM to denote specific CultureLLM and CultureLLM-
One for unified LLM.

Remark: Note that WVS is all in English, where we focus on cultural
differences in _opinions_ regardless of their native language. Thus, we do not
perform fine-tuning for other languages due to the shortage of their training
data and rely on cross-lingual transfer. Multilingual tasks for cultures can
still benefit from fine-tuned models in English, since the models can learn
the basic values from the opinions (moussaid2013social; jin2023better). Our
experiments in Section 5.1 further demonstrate that fine-tuning on English
data can outperform fine-tuning on native data that are translated from the
original English version.

##  4 Experiments

We fine-tuned a CultureLLM-One and $9$ specific CultureLLM for $9$ languages:
Arabic (Ar), Bengali (Bn), Chinese (Zh), English (En), German (De), Korean
(Ko), Portuguese (Pt), Spanish (Es), and Turkish (Tr). These cultures are
diverse and represent both high- and low-resource regimes and thus can serve
as representative evaluation.

###  4.1 Setup

Datasets. We adopt culture-related public datasets in specific languages for
evaluation. In total, we have $59$ test sets, covering $9$ languages and
containing $68,607$ test samples. We test on $56$ binary classification and
$3$ multi-classification tasks to detect: offensive language, hate speech,
stance, toxicity, threat, bias, abusive, and spam. For example, we ask LLMs to
judge whether the sentence contains offensive language, hate speech, or biased
speech. Details are shown in LABEL:tb-datasets and LABEL:sec-append-dataset.
Furthermore, we generate an open-ended generation dataset for evaluation in
Section 4.3.

Baselines and implementation details. We fine-tune CultureLLM using the
GPT-3.5 (0613) (chatgpt) fine-tuning API due to its efficiency and compared
with two state-of-the-art LLMs, namely Gemini pro (gemini) and GPT-4 (1104)
(openai2023gpt4). We also compare this with retrieval augmentation (RAG),
which enhances LLMs by searching for related information and adding it to
context (lewis2020retrieval). To implement RAG, we search for information
about each culture on Wikipedia and append them in a system prompt. Finally,
we fine-tuned CultureLLM using Llama-2-70b-chat (touvron2023llama) as the base
model for reproduction(Section 5.3). As for prompt setup, since our goal is to
make LLMs better align with people from different cultures, we add a system
prompt “You are an [x] chatbot that know [x] very well " where [x] is a
certain language before each input. For metrics, we use macro F1 score for all
tasks except for CValues (xu2023cvalues) where we use the automatic evaluation
script provided by the paper. For data augmentation, we set $k=5$, $m=2$, and
$\tau=0.8$.

Figure 3: The main results averaged by cultures (left) and by tasks (right).
Both CultureLLM and CultureLLM-One significantly outperform CultureLLM and
Gemini with CultureLLM achieving the best performance comparable to GPT-4.

###  4.2 Main Results

We present the average results for each culture and task in Figure 3
888Results on RAG are not shown since they are close to GPT-3.5. Since the
metrics are not the same (e.g., accuracy for CValues and F1 for other tasks),
we normalized each one and then averaged them. and more detailed results are
shown in Appendix LABEL:sec-append-exp. Our conclusions are as follows. First,
both specific and unified CultureLLM achieve a great improvement over other
approaches and specific CultureLLM achieves the best performance. Concretely
speaking, CultureLLM significantly outperforms GPT-3.5 (by $8.1$%), Gemini (by
$9.5$%), and RAG (by $7.94$%), achieving performance comparable to GPT-4 and
even better on some tasks. Second, CultureLLM-One exceeds GPT-3.5 by more than
$4\%$ on $59$ tasks, while inferior to culture-specific models, suggesting
that a single LLM might not be the best solution to solve low-resource
cultural tasks, since data from different cultures might intertwine with each
other. Third, in terms of cultures, CultureLLM achieves the best performance
in English, Chinese, and Spanish cultures while showing no obvious improvement
on Korean culture, where all four models have the similar performance. We
infer the reason could be that these base models have less exposure to Korean
culture. Finally, we analyze the performance on both low-resource and high-
resource language tasks. As shown in LABEL:fig-resource-results, CultureLLM
exhibits excellent performance in both types of tasks and outperforms GPT-4 on
a large scale in high-resource tasks.

###  4.3 Results on Open-ended Generation Tasks

To evaluate the performance of CultureLLM on open-ended tasks, we construct a
dataset using GPT-4, containing $65$ open-ended questions, which cover the
seven topics in WVS. The prompt setting for dataset generation can be found in
LABEL:sec-append-prompt. We evaluate the outputs of GPT-3.5 and CultureLLM
using Gemini Pro999We do not use GPT-4 to judge because it may prefer the
response from GPT series models.. We also devise a metric
$\text{WinRate}=(s_{CultureLLM}-s_{ChatGPT})/65$, where $s$ represents the
number of acceptances by Gemini Pro. Positive WinRate means CultureLLM wins
GPT-3.5 and vice versa. As shown in LABEL:tb-gen-results, CultureLLM performs
better than GPT-3.5 on $8$ out of $9$ cultures, demonstrating its
effectiveness in generation tasks.

Figure 4: Ablation study. ‘+WVS’ denotes the fine-tuned models using only the
50 samples from WVS, ‘+WVS+a’ denotes fine-tuning using the WVS samples and
the generated samples in step 1 of our data augmentation (i.e., using only
GPT-4 to generate), and ‘+WVS+a+b’ denotes the complete process of our
algorithm.

###  4.4 Ablation Study

We evaluate the effectiveness of our semantic data augmentation approach by
comparing it with the following variants: GPT-3.5, CultureLLM (WVS),
CultureLLM (WVS+a), and CultureLLM (WVS+a+b), where CultureLLM (WVS) denotes
the fine-tuned models using only the 50 samples from WVS, CultureLLM (WVS+a)
denotes fine-tuning using 50 WVS samples and the generated samples in step 1
of our data augmentation (i.e., only using semantic templates), and CultureLLM
(WVS+a+b) denotes the complete process of our algorithm. Note that ‘WVS+a’
denotes the naive baseline of only using GPT-4 to generate samples.

As shown in Figure 4, fine-tuning using only the 50 seed data from WVS can
inconsistently improve and impair performance on different tasks such as the
decrease on Korean tasks. While the WVS data are of very high quality, we see
gains even with our generated data which leads to improvements on most tasks.
Average performance on Korean tasks also get improved by CultureLLM. Figure 4
also demonstrates that the two steps in our semantic data augmentation
approach are useful and necessary.

###  4.5 Effectiveness Analysis

We analyze the effectiveness of CultureLLM by controlling the number of
generated data, computing the perplexity score, and presenting case studies.

First, we analyze the impact of the generation size. As illustrated by
chen2024self, the diversity and quality of datasets are important in training
LLMs. Hence, infinite or too many generated samples might hurt the performance
due to possible mode collapse. In this section, we control the number of
generated data and empirically analyze its impact. Specifically, we fine-tune
$4$ CultureLLM with $\\{0,100,500,1000\\}$ generated samples appended to the
original WVS data set. As shown in Figure 5, as the number of fine-tuning data
increases, performance across most of tasks get improved; but when the number
is greater than $500$, performance on all tasks decline.

Then we analyze the diversity of the generated data by computing two metrics:
perplexity (marion2023less; wang2023making) and diversity gain
(bilmes2022submodularity) (LABEL:sec-append-metric), as shown in the upper
right in Figure 5, where we observe the consistency between these two metrics
and the fine-tuning performance: the $500$ generated data lead to the best
perplexity and diversity gain. The reason may be that these $500$ samples are
enough for GPT-3.5 to understand the knowledge of seed data, and more samples
can cause overfitting and decreased performance. Additionally, although the
augmentation approach only generates different samples by varying sentence and
word styles, the diversities can also get increased. This suggests that the
variations in samples can improve the diversity of datasets.

As in the cases shown in LABEL:fig-more-cases, responses from GPT-3.5 often
analyze input from multiple perspectives and call on to be respectful and
kind, rather than provide clear and straightforward opinions. In some cases,
GPT-3.5 says it cannot determine the intentions behind the sentence without
context, while CultureLLM provides clear opinions most of the time. The reason
behind this may be that we fine-tune CultureLLM to learn opinions from
specific culture, so it can be more aligned with corresponding culture when
faced with cultural differences or cultural conflicts. However, GPT-3.5 is
aimed to serve people from different cultures. Thus, it prefers to give a
neutral response to not conflict with any cultures. However, the worst
consequence is that it can not provide useful responses on those problems
related to cultural differences.

###  4.6 The Effectiveness of the Augmented Data: A Human Study

Figure 5: Results on different numbers of fine-tuning samples with perplexity
score and diversity gain.

We analyze the effectiveness of the augmented data through human evaluators.
We hire $50$ people having high exposure to English (i.e., majoring in
English) to check if our generated sentences are semantically equivalent to
the seed data. The information of the participants and the training procedure
are in LABEL:sec-append-human. We sample $100$ pairs of (seed, generation)
samples and let each participant rank their similarities by giving a score of
$1$ to $5$, with $5$ representing the most similar. We also use GPT-4 and
Gemini Pro as evaluators. The average results in LABEL:tb-aug-efficient
demonstrate that the semantic similarity passes $96.5\%$, implying that our
augmentation approach can increase the quantity while retaining the
similarity.

We also conduct experiments on generation tasks. LABEL:fig-more-cases shows
the responses from GPT-3.5 and CultureLLM in four different cultures. The
results show that CultureLLM can generate more accurate, direct and useful
responses than GPT-3.5. To be specific, GPT-3.5 always generate long
responses, which don’t give useful information and just call on to be
respectful, while CultureLLM give accurate and direct responses. This is very
important for user experience.

##  5 Discussion

###  5.1 Augmenting Multilingual Data vs. English Data

Figure 6: The results of LLMs fine-tuned on English data (CultureLLM (En ft))
and on local languages (CultureLLM (local ft)). It can be observed that fine-
tuning on English data outperforms fine-tuning on local languages.

CultureLLM are fine-tuned on English data, since the training corpus of LLMs
such as the GPT series are mostly in English and English may be the choice for
LLMs to understand other cultures’ opinions. How about the performance of LLMs
fine-tuned in a culturally specific language? We also fine-tuned GPT-3.5
(chatgpt) on multilingual data which are translated from English data and
compare with CultureLLM. The results are shown in Figure 6, indicating that
the models fine-tuned in English perform better than the models fine-tuned in
other languages. The reason behind this may be the model’s inherent
capabilities in English have been shown to be superior (ahuja2023mega) than
other languages, which again emphasizes the importance of collecting large-
scale data for pre-training. This study demonstrates that in low-resource
settings without collecting large-scale training data, the augmentation
approach could be useful for fine-tuning.

###  5.2 Fine-tuning vs. Forgetting

A potential dilemma is that fine-tuning an LLM on specific tasks might face
catastrophic forgetting of its original capabilities. In this section, we
explore the forgetting of CultureLLM on two general datasets: BIG-Bench-Hard
(BBH) (suzgun2022challenging) and GSM8K (cobbe2021training). BBH contains $21$
tasks covering both semantic understanding and logical reasoning tasks. GSK8K
is a widely used data set to evaluate mathematical ability. For BBH, we sample
$100$ samples for each task to test, due to cost savings. We compare each
CultureLLM with the baseline model GPT-3.5 in Figure 7. Results show that
CultureLLM does not decrease performance in most of the general benchmarks,
but can even improve their results, such as the 21 BBH tasks. This suggests
that there might be some latent relations between the cultural data and the
general benchmarks, thus fine-tuning on cultural data can benefit general
reasoning abilities.

Figure 7: Analysis on catastrophic forgetting on BBH and GSM8K. The red line
denotes the results of GPT-3.5. For BBH, we show the average results of $21$
tasks in this figure. The x-axis represents models and the y-axis represents
performance.

###  5.3 CultureLLM on Open-sourced LLMs: Llama2

Although all main experiments in this work are performed using the OpenAI
fine-tuning API of GPT-3.5 (chatgpt) due to its efficiency and simplicity, our
CultureLLM also supports fine-tuning on open-source LLMs for better quality
control and reproducibility. In this section, we show an initial experiment
using Llama2-70b-chat as the base model to fine-tune a CultureLLM-Llama2-70b.
The results in Figure 8 show that CultureLLM-Llama-70b outperforms the base
Llama model by $2.17\%$ on average, showing the effectiveness of fine-tuning
CultureLLM on open-source models. The details of fine-tuning are in LABEL:sec-
append-llama2. The results indicate that CultureLLM is a general approach to
improve LLMs’ ability of cultural understanding.

###  5.4 Implication of This Research

In essence, recognizing and valuing cultural differences is paramount for the
enrichment of our global community. Embracing diversity stimulates innovation
and creativity, contributing to the development of novel ideas and solutions.
Our work contributes to solving the cultural difference problem in LLMs and
tackling the problem of data scarcity in low-resource cultures. Limited
availability of data from these cultures hinders understanding and addressing
specific needs and concerns. For example, lack of representation in datasets
may perpetuate biases and disparities, hindering the development of inclusive
technologies and services. Our approach represents an effective and resource-
saving method to bridge the data gap in low-resource cultures, empowering
these communities and enabling more accurate, inclusive, and impactful
decision-making processes.

Figure 8: The performance of CultureLLM-Llama-70b averaged by cultures (left)
and by tasks (right), which outperforms the vanilla Llama model by $2.17\%$ on
average.

##  6 Conclusion and Limitation

Cultural difference is essential to the prosperity of the world. In this
paper, we proposed CultureLLM, a cost-effective solution to fine-tune culture-
aware LLMs. We sampled a small number (50) of samples from World Value Survey
and then generated augmented data through our novel semantic data
augmentation. On $59$ datasets on $9$ cultures, CultureLLM outperformed
GPT-3.5 and Gemini with comparable or even better results than GPT-4.

This work has the following limitations. First, due to resource and time
constraints, we did not implement CultureLLM on large-scale open-source
models. Second, we only adopted classification tasks for evaluation since
multilingual generative tasks are expensive for automatic evaluation. Third,
for Arabic and Spanish, which are the main spoken languages for many
countries, we only select some representative countries to fine-tune for
reality factors, and this might also bring bias. Finally, the sample diversity
is only in sentence and word levels. In the future, we plan to add more
diversities to enrich the generated data.

## Impact Statement

This paper leveraged GPT-4 to generate sentences and synonyms, whose quality
were manually checked to ensure responsible usage. Throughout this paper, the
authors remain neutral towards the opinions from all different cultures and
respect their diversities. The human study was conducted following local laws
and regulations.