  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/.
