arXiv:2606. 01304v1 Announce Type: new Abstract: Hard negative mining has become the dominant strategy for training retrievers, yet it faces intrinsic limitations: negatives are bounded by corpus availability, selected by retriever score rather than diagnostic value, and increasingly contaminated by false positives as the retriever improves.
By Zhicheng Zhang, Jiwei Tang, Kuicai Dong, Xiaopeng Li, Jieming Zhu, Jingyu Li, Qianhui Zhu, Fengyuan Lu, Wang Jiaheng, Gang Wang, Hai-Tao Zheng, Zhaocheng Du
arXiv:2610.35045v1 Announce Type: cross
Abstract: Dense retrievers are commonly trained with hard negatives selected from lexical or dense retrieval results. Retrieval proximity, however, does not in...
By Ngoc Luyen Le, Marie-H\'el\`ene Abel, Bertrand Laforge
Task-Level Natural Language Priors as Learning Signals for Low-Resource LLM Training proposes Prior-Guided Tuning (PGT), a training approach that treats natural-language priors as auxiliary learning signals rather than just input context. The method introduces Contrastive Prior Steering (CPS), which adds positive and negative prior-conditioned auxiliary losses while preserving the original supervised objective. Experiments on AmbiMath, Jigsaw, and MNLI/HANS demonstrate that CPS consistently outperforms plain and prompt fine-tuning, achieving high accuracy and significant gains with limited training data.
By Jian Gao, Xiao Zhang, Xun Zhu, Miao Li, Ji Wu
The study investigates which properties of hard‑negative supervision predict dense‑retriever transfer before training. It compares lexical non‑coverage, frozen‑encoder similarity, hardness proxies, and their combinations across BM25, dense, LLM‑generated, and hybrid sources for MS MARCO training. The results show that mean lexical residuality, an IDF‑weighted measure of query‑token non‑coverage, consistently reproduces the aggregate transfer ordering, while semantic statistics alone or combined with lexical signals do not improve rank agreement.
By Aarush Sinha, Rahul Seetharaman, Aman Bansal
arXiv:2603. 20990v2 Announce Type: replace-cross Abstract: Hard-negative source selection for dense retrieval is usually decided only after fine-tuning and downstream evaluation.
By Aarush Sinha, Rahul Seetharaman, Aman Bansal
arXiv:2608.29604v1 Announce Type: cross
Abstract: Vision-language retrieval with CLIP-style dual encoders achieves strong cross-modal performance, yet practical accuracy often hinges on localized sem...
By Siyi Liu, Xiaorong Zhu, Enjun Du, Xinyu Zuo, Lisheng Duan, Haijin Liang, Jin Ma, Junfu Pu, Yongqi Zhang