Abstract Summary
Unlike short-form retrieval-augmented generation (RAG), such as factoid question answering, long-form RAG applications require retrieval to provide documents covering a wide range of relevant information. Automated report generation exemplifies this setting: it needs not only relevant information but also a more elaborate response with more information included. Yet, existing retrieval methods are primarily optimized for relevance rather than information coverage. To address this limitation, we propose LANCER, an \textbf{L}LM-based rer\textbf{A}nking method for \textbf{N}ugget \textbf{C}ov\textbf{ER}age. 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 sub-questions as possible at the top of the ranking. Our empirical results show that LANCER enhances the quality of retrieval as measured by nugget coverage metrics and can achieve better $\alpha$-nDCG and information coverage than other LLM-based reranking methods. Further analysis demonstrates that sub-question generation is one of the key components for optimizing coverage.
