aiqbench analysis
This arXiv paper is about training retrievers to follow natural-language instructions, a machine learning problem where the primary subject is clearly AI. The main contribution is a data-construction method, Natural Hard-Negative Mining, that reuses original positive passages as instruction-aware hard negatives instead of relying only on synthetically generated negatives. The claimed takeaway is that the gains come from the quality of this supervision rather than from model size or dataset volume, which is a useful signal for practitioners, though the results rest on benchmarks like InstructIR and FollowIR and should be checked for generalization beyond them.
Natural Hard-Negative Mining from Positives for Instruction-Following Retrieval
arXiv:2610.36900v1 Announce Type: new 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.
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.
Artificial Intelligence (cs.AI)
arXiv:2610.36900 [cs.AI]
(or arXiv:2610.36900v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.36900 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 238 words