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.
arXiv:2603. 20990v3 Announce Type: replace-cross Abstract: Hard-negative source selection for dense retrieval is usually decided only after fine-tuning and downstream evaluation.
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.
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.
The paper introduces Natural Hard-Negative Mining (NHNM) for instruction‑following information retrieval, a method that turns original positive passages into hard negatives under query‑compatible instructions. NHNM uses a pipeline of Candidate Cleaning, Reversal Construction, and Geometry Filtering to generate a compact, high‑contrast training set called NHNM‑MARCO with nearly 7,000 instances. Models fine‑tuned on NHNM‑MARCO, ranging from 568 M to 4 B parameters, achieve strong instruction‑following performance on InstructIR and FollowIR, matching or exceeding larger retrievers and demonstrating that the gains stem from natural hard‑negative supervision rather than model size or data volume.
arXiv:2606. 23695v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) grounds Large Language Models in external knowledge, yet current evaluations rely on discrete heuristics that suffer from ''epistemic blindness'' - failing to distinguish genuine contextual information extraction from parametric memory recall.
arXiv:2606. 29947v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as rerankers in recommender systems, with the expectation that semantic understanding will help in cold-start and long-tail regimes.
arXiv:2607. 18785v2 Announce Type: replace Abstract: As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable capability selection and execution.
The paper introduces SAFE‑QE, a training‑free, retriever‑agnostic method for query expansion that mitigates hallucinated terms from large language models. It uses a subsequence‑association filter to score candidate terms based on their reproducibility across randomised query contexts, down‑weighting terms that lack corpus support. SAFE‑QE then selects a broad yet consistent set of expansions via constrained coverage maximisation, and defines a new metric, the Hallucinated Expansion Term Rate, to quantify faithfulness. The approach matches state‑of‑the‑art ranking performance while injecting far fewer unsupported terms on TREC and BEIR benchmarks.
Coverage Under Contradiction: A Benchmark for Negation-Aware Retrieval introduces CoverageBench-Negate, a new benchmark that augments the existing CoverageBench with controlled contradictions. For each evidence nugget, a negated counterpart is created and its supporting documents are edited to support the negation while preserving context, resulting in about 63,000 nugget‑document pairs across six datasets. The benchmark includes rich construction metadata, LLM judgments, and a human audit, and baseline experiments show BM25 can distinguish original from edited evidence better than dense retrievers.
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...
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...
arXiv:2604.18835v2 Announce Type: replace-cross Abstract: We propose a scalable, multifactorial experimental framework that systematically probes LLM sensitivity to subtle semantic changes in pairwis...