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This paper introduces an attribution-guided query rewriting method to improve the robustness of neural retrievers when faced with underspecified or ambiguous queries. The approach computes gradient-based token attributions from the retriever to identify problematic query components and then uses these attributions to guide an LLM in rewriting the query. Experiments on BEIR collections demonstrate that this method consistently improves retrieval effectiveness compared to existing query rewriting and explainability-based techniques, especially for implicit or ambiguous information needs.
By closing the loop between neural retrievers and LLMs via attribution guidance, this method achieves significant gains in retrieval effectiveness, particularly for ambiguous queries.
Neural retrievers are effective but brittle: underspecified or ambiguous queries can misdirect ranking even when relevant documents exist. Existing approaches address this brittleness only partially: LLMs rewrite queries without retriever feedback, and explainability methods identify misleading tokens but are used for post-hoc analysis. We close this loop and propose an attribution-guided query rewriting method that uses token-level explanations to guide query rewriting. For each query, we compute gradient-based token attributions from the retriever and then use these scores as soft guidance in a structured prompt to an LLM that clarifies weak or misleading query components while preserving intent. Evaluated on BEIR collections, the resulting rewrites consistently improve retrieval effectiveness over strong baselines, with larger gains for implicit or ambiguous information needs.
