IndoBias: A Dual Track Culturally Grounded Benchmark for LLMs Bias Evaluation in Indonesian Languages
Summary
IndoBias is a new culturally-grounded benchmark designed to evaluate Large Language Model (LLM) bias in Indonesian and three local languages: Javanese, Sundanese, and Makasar. This benchmark addresses a critical gap in assessing representational fairness and localized stereotypes within Indonesia's diverse sociocultural landscape, which includes over 1300 ethnic groups and 700 indigenous languages. IndoBias employs a dual-perspective evaluation, featuring a depth-oriented track with contrastive-pairs and a breadth-oriented, generation-based track grounded in social science frameworks like SPI, O*NET, and WGI. Initial results indicate that existing LLMs, especially decoder models, show strong bias towards prototypical Indonesian sentences. Local languages exhibit higher bias under the Ideology and Religion categories. Furthermore, LLM responses demonstrate non-uniform Stereotype Polarity when prompted with various local entities. The study also found that Common Crawl texts introduce more bias during Indonesian pretraining compared to human-reviewed articles, and introducing local languages generally increases bias.
Key takeaway
For NLP Engineers and AI Ethicists developing or deploying LLMs in diverse linguistic regions, you must prioritize culturally-grounded bias evaluations. Your models, especially decoder architectures, likely carry significant biases in languages like Indonesian and its local variants, particularly concerning ideology and religion. You should carefully select pretraining data, favoring human-reviewed sources over broad crawls to mitigate bias. Implement dual-track benchmarks to uncover nuanced stereotype polarities and ensure representational fairness.
Key insights
Culturally-grounded benchmarks are crucial for evaluating LLM bias in diverse, multilingual contexts like Indonesia.
Principles
- LLMs exhibit strong bias in Indonesian and local languages.
- Pretraining data sources impact bias levels significantly.
- Bias varies across categories and local entities.
Method
IndoBias uses dual-track evaluation: depth-oriented contrastive-pairs and breadth-oriented generation based on SPI, O*NET, WGI frameworks.
In practice
- Use culturally-grounded benchmarks for LLM fairness.
- Scrutinize pretraining data sources for bias introduction.
- Analyze bias across specific categories like Ideology/Religion.
Topics
- LLM Bias Evaluation
- Indonesian Language Models
- Multilingual NLP
- Cultural Grounding
- Representational Fairness
- Pretraining Data Bias
Best for: Research Scientist, AI Scientist, NLP Engineer, AI Ethicist
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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.