arXiv Computation and Language October 16, 2026 By Kailin Jiang, Lei Liu, Jian Xi, Hui Xu, Junlin Liu, Baochen Fu, Bin Li, Vichwang, Yu Lu, Haibo Shi

Beyond Relevance-Centric Retrieval: Rubric-Oriented Document Set Selection and Ranking

Read the original on arXiv Computation and Language →

The paper introduces SetwiseEvalKit, a benchmark with nine dimensions and 28K rubrics for evaluating document sets, highlighting that current rerankers only achieve up to 45% coverage and struggle with cross‑document coordination. It proposes Rubric4Setwise, a training‑free method that transforms rubric criteria into selection signals, outperforming other methods in downstream generation tasks across both short‑form and long‑form scenarios. The study demonstrates the importance of evaluating and optimizing document sets as a whole rather than independently.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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