Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy
<|reference_start|>Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy: Utility and topical relevance are critical measures in information retrieval (IR), reflecting system and user perspectives, respectively. While topical relevance has long been emphasized, utility is a higher standard of relevance and is more useful for facilitating downstream tasks, e.g., in Retrieval-Augmented Generation (RAG). When we incorporate utility judgments into RAG, we realize that the topical relevance, utility, and answering in RAG are closely related to the three types of relevance that Schutz discussed from a philosophical perspective. They are topical relevance, interpretational relevance, and motivational relevance, respectively. Inspired by the dynamic iterations of the three types of relevance, we propose an Iterative utiliTy judgmEnt fraMework (ITEM) to promote each step of the cycle of RAG. We conducted extensive experiments on multi-grade passage retrieval and factoid question-answering datasets (i.e., TREC DL, WebAP, and NQ). Experimental results demonstrate significant improvements in utility judgments, ranking of topical relevance, and answer generation upon representative baselines, including multiple single-shot utility judging approaches. Our code and benchmark can be found at https://anonymous.4open.science/r/ITEM-B486/.<|reference_end|>
@article{zhang2024iterative,
    title={Iterative Utility Judgment Framework via LLMs Inspired by Relevance in
  Philosophy},
    author={Hengran Zhang, Keping Bi, Jiafeng Guo, Xueqi Cheng},
    journal={arXiv preprint arXiv:2406.11290},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11290},
    primaryClass={cs.IR cs.AI cs.CL cs.LG}
}