Iterative Utility-Focused Evidence Refinement for Retrieval-Augmented Generation

ACL ARR 2026 January Submission9610 Authors

Keywords: Evidence Refinement for Retrieval-Augmented Generation, Utility Judgments, Iterative Utility-Focused Evidence Refinement

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 components—relevance ranking derived from retrieval models, utility judgments, and answer generation—aligns with Schutz’s philosophical system of relevances, which encompasses three types of relevance representing different levels of human cognition that enhance each other. These three RAG components also reflect three cognitive levels for LLMs in question-answering. Therefore, we propose an Iterative uTility-focused Evidence refineMent (ITEM) to promote each step in RAG. We conducted extensive experiments on retrieval (TREC DL, WebAP), utility judgment task (GTI-NQ), and factoid question-answering (NQ) datasets. Experimental results demonstrate improvements of \modelname in utility judgments, ranking, and answer generation upon representative baselines.

Paper Type: Long

Research Area: Information Extraction and Retrieval

Research Area Keywords: re-ranking, retrieval-augmented generation, passage retrieval

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 9610