LLM-Oriented Information Retrieval: A Denoising-First Perspective
Researchers propose that information retrieval for LLMs requires a fundamental shift toward denoising—prioritizing signal quality over quantity—because unlike humans, language models are vulnerable to hallucinations when processing noisy or irrelevant data within limited context windows. The paper introduces a four-stage framework addressing IR challenges from inaccessibility to unverifiability, with practical applications across RAG systems, coding agents, and multimodal understanding.