MULTI3IR: A Benchmark for Multi-perspective Multi-domain Multi-modal Information Retrieval

seokwon, song, Sohyeon Kim, Gunhee Kim

MULTI3IR: A Benchmark for Multi-perspective Multi-domain Multi-modal Information Retrieval: 9 upvotes on Hugging Face Daily Papers, #20 of 36 papers on 2026-09-03. Day-by-day upvote history.

Information retrieval (IR) increasingly targets open-ended queries that admit diverse perspectives. Existing IR benchmarks, however, focus primarily on closed-ended queries, while even open-ended benchmarks largely consist of queries whose supporting documents span a single subject domain and modality. We introduce Multi^3IR, a benchmark that evaluates how well retrievers cover the multifaceted perspectives of open-ended queries across diverse domains and modalities. It comprises 104.9K Stack Exchange queries, each annotated with perspective descriptions that capture the query's implicit viewpoints. We further propose SPIN, a parameter- and label-efficient method that learns noise vectors to steer embeddings toward diverse yet meaningful semantic directions. Experiments show that existing multimodal retrievers suffer from single-perspective bias, while SPIN substantially improves perspective coverage on Multi^3IR and generalizes well to unseen open-ended IR benchmarks. The dataset and experimental code are available at https://github.com/seokwon99/Multi3IR.

Paper page on Hugging Face · arXiv

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