Measuring Cultural Bias in Large Language Models: CAMeL Study

Core Concepts

Large language models exhibit bias towards Western entities in non-English, non-Western languages, impacting cultural adaptation and fairness.

Abstract

The study introduces CAMeL, a dataset to measure cultural biases in LMs. It analyzes stereotypes, fairness in NER and sentiment analysis, and text infilling performance. Pre-training corpora analysis reveals Western-centric content prevalence.

Introduction:

• Multicultural world enriches global community.
• Importance of LMs understanding cultural distinctions.
• Need for culturally-aware LMs fostering global connection.

Data Extraction:

• "CAMeL provides a foundation for measuring cultural biases in LMs through both extrinsic and intrinsic evaluations."
• "We analyze the cross-cultural performance of 16 different LMs on tasks such as story generation, NER, and sentiment analysis."

Related Work:

• Recent efforts on examining moral knowledge encoded in LMs.
• Studies on cross-cultural differences in values and beliefs by LMs.

Construction of CAMeL:

• Collection of culturally-relevant entities from Wikidata and CommonCrawl.
• Retrieval of natural contexts from Twitter/X for prompts creation.

Measuring Cultural Bias in LMs:

• Analysis of stereotypes in generated stories about Arab and Western entities.
• Examination of fairness in NER and sentiment analysis tasks.
• Benchmarking the capability of LMs on culturally appropriate text infilling.

Analyzing Arabic Pre-training Data:

• Comparison of cultural relevance across six Arabic corpora commonly used for pre-training.
• Training 4-gram LMs to compute average CBS for each corpus using CAMeL-Co prompts.

Stats

CAMeL provides a foundation for measuring cultural biases in LMs through both extrinsic and intrinsic evaluations.
We analyze the cross-cultural performance of 16 different LMs on tasks such as story generation, NER, and sentiment analysis.

Quotes

"LMs exhibit bias towards Western entities even when prompted by contexts uniquely suited for Arab culture-associated entities."
"Do LM's exhibit bias towards Western entities in non-Western languages?"
"Most multilingual LM's showed a higher CBS compared with monolingual LM's."

Key Insights Distilled From

Having Beer after Prayer? Measuring Cultural Bias in Large Language Models

by Tarek Naous,... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2305.14456.pdf

Deeper Inquiries

How can LM's be improved to adapt better to diverse cultural contexts?

To enhance the adaptation of Language Models (LMs) to diverse cultural contexts, several strategies can be implemented:

Diverse Training Data: Including a more extensive and varied range of cultural content in the training data can help LMs learn about different cultures and their nuances. This would involve incorporating datasets from various regions, languages, and demographics.

Cultural Context Embeddings: Developing specialized embeddings that capture cultural context could assist LMs in understanding and generating content specific to different cultures. These embeddings could encode information about traditions, beliefs, values, etc.

Prompt Customization: Tailoring prompts with explicit references to specific cultures during training can guide LMs on how to respond appropriately when faced with culturally relevant queries or tasks.

Fine-tuning Techniques: Utilizing fine-tuning methods that focus on adjusting model parameters based on specific cultural attributes or biases identified during evaluation can help improve performance in diverse cultural settings.

Bias Mitigation Strategies: Implementing bias mitigation techniques such as debiasing algorithms or fairness constraints during training can reduce the influence of stereotypes or unfair representations in model outputs across different cultures.

What are the implications of biased language models on society?

Biased language models pose significant implications for society:

Reinforcement of Stereotypes: Biased models perpetuate stereotypes by associating certain groups with particular characteristics or behaviors, leading to misrepresentations and reinforcing societal prejudices.

Inequality Reinforcement: Biased outputs from language models may exacerbate existing inequalities by marginalizing certain groups or communities through inaccurate portrayals or discriminatory predictions.

Impact on Decision-making Processes: Biases in language models can influence decision-making processes in various fields such as hiring practices, loan approvals, legal judgments, etc., potentially leading to unjust outcomes for individuals belonging to marginalized groups.

Social Division:

Biased language models may contribute to social division by promoting divisive narratives or misinformation that target specific communities.

6Trust Erosion:

The presence of biases erodes trust in AI systems and undermines confidence in their ability to provide fair and unbiased solutions across diverse populations.

How can we ensure fair representation across different cultures within language technology?

Ensuring fair representation across different cultures within language technology involves several key steps:
1- Diverse Dataset Curation: Curate datasets that encompass a wide range of cultural perspectives,
languages,demographics,and experiences.This diversity will enable more inclusive learning for
language technologies.
2- Bias Detection Mechanisms: Implement robust mechanisms for detecting biases within trained
models.These mechanisms should continuously monitor model behavior,to identify any instances
of unfairness towards particular cultures.
3- Cultural Sensitivity Training: Provide explicit training data examples focusing on culturally-
sensitive topics.This targeted approach helps machines understand nuances relatedto cultureand
avoids stereotypical assumptions.
4- Community Engagement: Engage with local communities,researchers,and stakeholders from
different backgrounds.Seek feedbackand inputonhowlanguage technologiescanbetter represent
diverseculturesand addressanyconcernsregardingbiasor discrimination.
5-Evaluation Metrics Development: Develop evaluation metrics specifically designedto assessfairness,
cultural sensitivity,and inclusivityacross multiplecultures.Thesemetricsshouldbeusedtomeasuretheperformanceoflanguage technologiesinadiversearrayofcontexts
By implementing these strategies along with ongoing research into bias detection,fairness-awaremodeltraining,and communityengagement,the goal offairrepresentationacrossdifferentcultureswithinlanguagetechnologycanbeprogressivelyachieved