ImpliRet: Benchmarking the Implicit Fact Retrieval Challenge

• URL: http://arxiv.org/abs/2506.14407v2
• Date: Tue, 15 Jul 2025 13:16:23 GMT
• Title: ImpliRet: Benchmarking the Implicit Fact Retrieval Challenge
• Authors: Zeinab Sadat Taghavi, Ali Modarressi, Yunpu Ma, Hinrich Schütze,
• Abstract summary: ImpliRet is a benchmark that shifts the reasoning challenge to document-side processing.<n>We evaluate a range of sparse and dense retrievers, all of which struggle in this setting.
• Score: 49.65993318863458
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval systems are central to many NLP pipelines, but often rely on surface-level cues such as keyword overlap and lexical semantic similarity. To evaluate retrieval beyond these shallow signals, recent benchmarks introduce reasoning-heavy queries; however, they primarily shift the burden to query-side processing techniques -- like prompting or multi-hop retrieval -- that can help resolve complexity. In contrast, we present ImpliRet, a benchmark that shifts the reasoning challenge to document-side processing: The queries are simple, but relevance depends on facts stated implicitly in documents through temporal (e.g., resolving "two days ago"), arithmetic, and world knowledge relationships. We evaluate a range of sparse and dense retrievers, all of which struggle in this setting: the best nDCG@10 is only 14.91%. We also test whether long-context models can overcome this limitation. But even with a short context of only thirty documents, including the positive document, GPT-o4-mini scores only 55.54%, showing that document-side reasoning remains a challenge. Our codes are available at: github.com/ZeinabTaghavi/IMPLIRET

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