NHNM：把正样本变成难负样本，改进指令式检索训练
Natural Hard-Negative Mining from Positives for Instruction-Following Retrieval
这篇论文教你怎么把检索训练里的正样本直接改造成难负样本，568M 小模型微调后能打更大的检索器，做 RAG 的可以看看。
论文提出 Natural Hard-Negative Mining（NHNM），把原始正例段落改造成自然指令难负样本，通过 Candidate Cleaning、Reversal Construction、Geometry Filtering 三步自动构建监督数据。基于 MSMARCO 生成了约 7K 实例的 NHNM-MARCO 训练集。在 InstructIR 和 FollowIR 基准上，568M 到 4B 参数的模型微调后达到或超过更大的检索器。消融实验显示收益来自自然难负样本监督，而非模型规模或数据量。
Natural Hard-Negative Mining from Positives for Instruction-Following Retrieval
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.
