LANCER: LLM Reranking for Nugget Coverage
Jia-Huei Ju, Fran\c{c}ois G. Landry, Eugene Yang, Suzan Verberne, and Andrew Yates

TL;DR
LANCER is a novel LLM-based reranking method designed to improve document retrieval coverage for long-form report generation by predicting sub-questions and ranking documents accordingly.
Contribution
It introduces a new reranking approach that focuses on maximizing nugget coverage, addressing limitations of relevance-focused retrieval methods.
Findings
LANCER achieves higher nugget coverage metrics than existing reranking methods.
Sub-question generation significantly improves retrieval quality.
Empirical results demonstrate enhanced information coverage in retrieval tasks.
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…
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Taxonomy
TopicsTopic Modeling · Expert finding and Q&A systems · Information Retrieval and Search Behavior
