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arXiv · 2605.28522

Search for Coverage: Learning Coverage-Aware Retrieval with Augmented Sub-Question Answerability

Jia-Huei Ju·Eugene Yang·Trevor Adriaanse·Suzan Verberne·Andrew Yates
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Abstract

Long-form Retrieval-Augmented Generation (RAG) brings the challenge of coverage-based ranking, because ranking methods must ensure the inclusion of comprehensive relevant nuggets (i.e., facts), which can thereby be synthesized into a comprehensive output. In this work, we propose CoveR (Our code is available at https://github.com/DylanJoo/CoveR ) a dense retrieval method optimized for coverage-aware retrieval scenarios. CoveR is a bi-encoder trained with the coverage-based contrastive and distillation objectives, which enables CoveR to capture diverse aspects of information needs. To train CoveR, we create the SCOPE dataset, (Our training data is available at https://huggingface.co/datasets/DylanJHJ/scope ) which comprises 90K training pairs from Researchy Questions with synthetic coverage signals augmented from sub-question answerability judgments generated by LLMs. Our empirical experiments show that CoveR enhances nugget coverage by 10\% over strong dense retrieval baselines without sacrificing its relevance-based retrieval capability. Further ablation studies validate the importance of our proposed learning method, showing that CoveR achieves a superior trade-off between relevance- and coverage-based ranking, which is essential for long-form RAG.

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Jia-Huei Ju, Eugene Yang, Trevor Adriaanse, Suzan Verberne, Andrew Yates. 2026-05-27. Search for Coverage: Learning Coverage-Aware Retrieval with Augmented Sub-Question Answerability. https://doi.org/10.1145/3805712.3809752

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