When Hard Negatives Hurt: Bridging the Generative-Discriminative Gap in Hard Negative Synthesis for Retrieval
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. LLM-based synthesis offers a principled alternative, where negatives that are unconstrained, targeted, and free from false positive risk. But we show that naively incorporating generated negatives into contrastive learning often degrades retrieval performan
The proliferation of LLM-based systems leads to new approaches for data generation, making a principled re-evaluation of 'hard negative' synthesis in retrieval systems timely.
Improving the efficacy of retrieval systems directly impacts the performance of many AI applications, including question-answering, search, and recommendation, thus influencing productivity and innovation across sectors.
The understanding of how to effectively train retrieval models shifts from relying solely on corpus-bound negatives to intelligently synthesized negatives, provided 'generative-discriminative gap' issues are addressed.
- · AI model developers
- · Search engine companies
- · Retrieval-Augmented Generation (RAG) system providers
- · Companies relying on outdated retrieval training methods
- · Generative AI models producing low-quality negative samples
More robust and accurate AI retrieval systems emerge, improving the quality of information access.
This technical advancement could accelerate the development and deployment of more sophisticated AI agents that rely on high-fidelity information retrieval.
Improved retrieval could enable new forms of automated knowledge work, further pressing the 'AI Agents' narrative.
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Read at arXiv cs.LG