Papers by Ramit Aditya
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. |