AI RESEARCH
An Iterative Utility Judgment Framework Inspired by Philosophical Relevance via LLMs
arXiv CS.AI
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ArXi:2406.11290v3 Announce Type: replace-cross 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.