arXiv AI
CA-HNM: Constraint-Aware Hard Negative Mining for Dense Retrieval with Structured Domain Models
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Natural Hard-Negative Mining from Positives for Instruction-Following Retrieval
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.
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The paper investigates modular entity disambiguation by separating candidate retrieval from entity selection. It compares sparse retrieval (BM25), Web KB search, and a dense retriever, all paired with large language model selectors. Results show that a training‑free BM25 retriever combined with an LLM selector achieves state‑of‑the‑art performance on the ZELDA benchmark, and the modular approach enables abstention when retrieval fails.
arXiv Machine Learning
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Guiding the coarse levels of semantic IDs makes the fine levels learnable
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arXiv AI
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E-SENS: Exclusion-Sensitive Penalization for Negative-Constraint Retrieval
E‑SENS is a training‑free reranking technique designed to improve negative‑constraint retrieval in retrieval‑augmented language models. It works by extracting a compact ‘trap query’ that represents the excluded concept and subtracting the similarity of this trap query from the original retrieval score, thereby reducing the influence of documents that mention the excluded concept. Experiments on the ExcluIR benchmark demonstrate that E‑SENS achieves a clear recall‑violation trade‑off across four embedding models and effectively reduces trap retrieval while preserving recall.
arXiv AI
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$\mathrm{ECI}_{\mathrm{sem}}$: Semantic Residual Effective Contrastive Information for Evaluating Hard Negatives
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.