Bias Evaluation in Large Language Models

This is a code repository for the paper "No LLM is Free From Bias: A Comprehensive Study of Bias Evaluation in Large Language Models".

• Tinyllama
• Phi3.5
• Mistral7b
• Llama8b

using the following datasets:

• StereoSet
• WinoBias
• UnQover
• CrowS-Pairs
• Real Toxicity Prompts(RTP)
• Equity Evaluation Corpus(EEC)

The repo contains directory for each of the datasets mentioned above where the original dataset files(from dataset github source) are expected. Following is the list of dataset (can be cloned from the provided source links) and dataset generation script files (provided in this repo) expected for bias evaluation:

• StereoSet: dev.json
• WinoBias: anti_stereotyped_type1.txt.dev anti_stereotyped_type1.txt.test anti_stereotyped_type2.txt.dev anti_stereotyped_type2.txt.test pro_stereotyped_type1.txt.dev pro_stereotyped_type1.txt.test pro_stereotyped_type2.txt.dev pro_stereotyped_type2.txt.test
• UnQover: gen_script.sh
• CrowS-Pairs: crows_pairs_anonymized.csv
• Real Toxicity Prompts: real_toxicity_prompts.jsonl
• Equity Evaluation Corpus: Equity_Evaluation_Corpus.csv

If you find our work useful, please consider citing:

@inproceedings{kumar2025nollm,
    title     = {No LLM is Free From Bias: A Comprehensive Study of Bias Evaluation in Large Language Models},
    author    = {Kumar, Charaka Vinayak and Urlana, Ashok and Kanumolu, Gopichand and Garlapati, Bala Mallikarjunarao and Mishra, Pruthwik},
    booktitle = {Proceedings of the ECML-PKDD Workshop on Bias and Fairness in AI (BIAS)},
    year      = {2025},
    url       = {https://llms-bias.github.io/bias/}
}