arXiv cs.AI· Chaolang Li, Jingyu Li, Zhaocheng Du, Xuyang Xie, Gang Wang, Pengwen Dai·· 3 小时前AI 评分38
指令遵循检索研究提出 Natural Hard-Negative Mining（NHNM）方法
Natural Hard-Negative Mining from Positives for Instruction-Following Retrieval
AI 导读
一项 arXiv 研究提出 Natural Hard-Negative Mining（NHNM），将原始正例改造为自然指令难负例，为指令遵循信息检索（IFIR）生成高质量对比监督。
正文 · 原文
Abstract:Instruction-following information retrieval (IFIR) requires retrievers to rank documents by both a query and its instance-specific instructions, yet instruction-conditioned contrastive supervision remains scarce. Existing synthetic methods typically fix a positive passage and generate additional negatives, overlooking the hard-negative potential of the original positive. We propose Natural Hard-Negative Mining (NHNM), which promotes convertible negatives under query-compatible instructions and repurposes original positives as natural instruction hard negatives. Its automated Candidate Cleaning, Reversal Construction, and Geometry Filtering pipeline produces compact, high-contrast supervision aligned with the pretrained retrieval space. Applied to MSMARCO, NHNM yields NHNM-MARCO, an IFIR training set of nearly 7K instances. On InstructIR and FollowIR, models with 568M to 4B parameters fine-tuned on NHNM-MARCO achieve strong instruction-following performance and match or surpass substantially larger retrievers in several settings. Controlled analyses and ablations attribute these gains to natural hard-negative supervision rather than model scale or training-data volume.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.36900 [cs.AI] |
| (or arXiv:2610.36900v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36900 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Chaolang Li [view email]
[v1]
Tue, 27 Oct 2026 09:31:15 UTC (1,656 KB)
来源：arXiv cs.AI · arxiv.org