评分 5.0 · 来源：cs.AI updates on arXiv.org · 发布于 2026-04-14
评分依据：中等质量：常规学术论文，有适度参考价值
An Iterative Utility Judgment Framework Inspired by Philosophical Relevance via LLMs
arXiv:2406.11290v3 Announce Type: replace-cross Abstract: Relevance and utility are two frequently used measures to evaluate the effectiveness of an information retrieval (IR) system. Relevance emphasizes the aboutness of a result to a query, while utility refers to the result’s usefulness or value to an information seeker. In retrieval-augmented generation (RAG), high-utility results should be prioritized to feed to LLMs due to their limited input bandwidth. Re-examining RAG’s three core…