MULTI3IR：多視角多領域多模態資訊檢索基準

摘要

資訊檢索（IR）日益聚焦於允許多樣觀點的開放式查詢。然而，現有的 IR 基準主要專注於封閉式查詢，即便是開放式基準，其查詢的支持文件也大多侷限於單一主題領域與模態。我們提出 Multi^3IR，這是一個評估檢索器如何涵蓋開放式查詢在多樣領域與模態下多面向觀點的基準。它包含 104.9K 個 Stack Exchange 查詢，每個查詢皆附有觀點描述之註釋，用以捕捉其隱含觀點。我們進一步提出 SPIN，一種參數與標籤兼具效率的方法，透過學習噪聲向量，將嵌入引導至多樣且具意義的語意方向。實驗顯示，現有的多模態檢索器存在單一觀點偏誤，而 SPIN 在 Multi^3IR 上顯著提升了觀點涵蓋率，並能良好地泛化至未見過的開放式 IR 基準。資料集與實驗程式碼可在 https://github.com/seokwon99/Multi3IR 取得。

English

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.