Paper Detail

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

Seokwon Song, Sohyeon Kim, Gunhee Kim

arxiv Score 12.3

Published 2026-08-31 · First seen 2026-09-01

General AI

Abstract

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$^3$IR, 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$^3$IR and generalizes well to unseen open-ended IR benchmarks. The dataset and experimental code are available at https://github.com/seokwon99/Multi3IR.

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BibTeX

@article{song2026multi3ir,
  title = {MULTI3IR: A Benchmark for Multi-perspective Multi-domain Multi-modal Information Retrieval},
  author = {Seokwon Song and Sohyeon Kim and Gunhee Kim},
  year = {2026},
  abstract = {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\$\textasciicircum{}3\$IR, 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 },
  url = {https://arxiv.org/abs/2608.30949},
  keywords = {cs.IR},
  eprint = {2608.30949},
  archiveprefix = {arXiv},
}

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