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arxiv:2502.06006

FactIR: A Real-World Zero-shot Open-Domain Retrieval Benchmark for Fact-Checking

Published on Feb 9
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Abstract

A real-world retrieval benchmark, FactIR, is introduced for fact-checking using human-annotated production logs, evaluating state-of-the-art retrieval models in zero-shot scenarios.

AI-generated summary

The field of automated fact-checking increasingly depends on retrieving web-based evidence to determine the veracity of claims in real-world scenarios. A significant challenge in this process is not only retrieving relevant information, but also identifying evidence that can both support and refute complex claims. Traditional retrieval methods may return documents that directly address claims or lean toward supporting them, but often struggle with more complex claims requiring indirect reasoning. While some existing benchmarks and methods target retrieval for fact-checking, a comprehensive real-world open-domain benchmark has been lacking. In this paper, we present a real-world retrieval benchmark FactIR, derived from Factiverse production logs, enhanced with human annotations. We rigorously evaluate state-of-the-art retrieval models in a zero-shot setup on FactIR and offer insights for developing practical retrieval systems for fact-checking. Code and data are available at https://github.com/factiverse/factIR.

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