Papers by Farsheed Haque
Discovering and Mitigating Indirect Bias in Attention-Based Model Explanations (2024.findings-naacl)
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| Challenge: | Discrimination is the unfair treatment or prejudice directed towards individuals, groups, or certain ideas or beliefs, intentionally or unintentionally. |
| Approach: | They propose an algorithm to detect and mitigate indirect bias in transformer models by leveraging attention explanations. |
| Outcome: | The proposed algorithm shows that it is more accurate than traditional fairness metrics and that it can be used to mitigate bias in transformer models. |
Fine-tuning LLMs with Cross-Attention-based Weight Decay for Bias Mitigation (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) excel in natural language processing tasks but often propagate societal biases from their training data, leading to discriminatory outputs. |
| Approach: | They propose a method that modifies the LLM architecture to mitigate bias by adjusting the attention weights of sensitive tokens. |
| Outcome: | The proposed method can handle multiple sensitive attributes and does not require full knowledge of sensitive tokens presented in the dataset. |
Fair RAG: End-to-End Fairness Across Retrieval and Generation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) can amplify demographic bias by generating skewed context . prior work treats fairness in retrieval or generation in isolation, leaving end-to-end fairness underexplored . |
| Approach: | They propose a pipeline that jointly controls both retrieval and generation stages . large language models can handle a broad set of inference tasks, they argue . |
| Outcome: | The proposed pipeline reduces retriever-side skew and achieves lowest generator-side disparity while preserving utility. |