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arXiv · 2601.22008

LANCER: LLM Reranking for Nugget Coverage

Jia-Huei Ju·François G. Landry·Eugene Yang·Suzan Verberne·Andrew Yates
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Abstract

Unlike short-form retrieval-augmented generation (RAG), such as factoid question answering, long-form RAG requires retrieval to provide documents covering a wide range of relevant information. Automated report generation exemplifies this setting: it requires not only relevant information but also a more elaborate response with comprehensive information. Yet, existing retrieval methods are primarily optimized for relevance ranking rather than information coverage. To address this limitation, we propose LANCER, an LLM-based reranking method for nugget coverage. LANCER predicts what sub-questions should be answered to satisfy an information need, predicts which documents answer these sub-questions, and reranks documents in order to provide a ranked list covering as many information nuggets as possible. Our empirical results show that LANCER enhances the quality of retrieval as measured by nugget coverage metrics, achieving higher $\alpha$-nDCG and information coverage than other LLM-based reranking methods. Our oracle analysis further reveals that sub-question generation plays an essential role.

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Jia-Huei Ju, François G. Landry, Eugene Yang, Suzan Verberne, Andrew Yates. 2026-01-29. LANCER: LLM Reranking for Nugget Coverage. https://arxiv.org/abs/2601.22008

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