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Behind the Words: Unmasking Bias Lurking in LLMs and Data
Naina Dhingra (Savitribai Phule Pune University), Gargi Kale (Savitribai Phule Pune University), Harshada Chavan (Savitribai Phule Pune University), Sandhya Arora (Savitribai Phule Pune University)
Abstract: This study examines hidden biases in large language models (LLMs). Three popular models-RoBERTa, BERT, and GPT-2-were evaluated using StereoSet, CrowS-Pairs, and Indian-BhED, datasets designed to expose biases. Our comparative analysis reveals clear variations in how these models encode and reproduce bias, underscoring the difficulty of mitigation. The results highlight the need for bias-aware evaluation. By contributing empirical evidence, this work advances the discussion on ethical AI and points to directions for building more responsible language technologies.