[2511.18749] Large Language Models Require Curated Context for Reliable Political Fact-Checking -- Even with Reasoning and Web Search

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Computer Science > Computation and Language

arXiv:2511.18749 (cs)

[Submitted on 24 Nov 2025]

Title:Large Language Models Require Curated Context for Reliable Political Fact-Checking -- Even with Reasoning and Web Search

Authors:Matthew R. DeVerna, Kai-Cheng Yang, Harry Yaojun Yan, Filippo Menczer<br>View a PDF of the paper titled Large Language Models Require Curated Context for Reliable Political Fact-Checking -- Even with Reasoning and Web Search, by Matthew R. DeVerna and 3 other authors

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Abstract:Large language models (LLMs) have raised hopes for automated end-to-end fact-checking, but prior studies report mixed results. As mainstream chatbots increasingly ship with reasoning capabilities and web search tools -- and millions of users already rely on them for verification -- rigorous evaluation is urgent. We evaluate 15 recent LLMs from OpenAI, Google, Meta, and DeepSeek on more than 6,000 claims fact-checked by PolitiFact, comparing standard models with reasoning- and web-search variants. Standard models perform poorly, reasoning offers minimal benefits, and web search provides only moderate gains, despite fact-checks being available on the web. In contrast, a curated RAG system using PolitiFact summaries improved macro F1 by 233% on average across model variants. These findings suggest that giving models access to curated high-quality context is a promising path for automated fact-checking.

Subjects:

Computation and Language (cs.CL); Computers and Society (cs.CY); Information Retrieval (cs.IR)

Cite as:<br>arXiv:2511.18749 [cs.CL]

(or<br>arXiv:2511.18749v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2511.18749

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arXiv-issued DOI via DataCite

Submission history<br>From: Matthew R. DeVerna [view email]<br>[v1]<br>Mon, 24 Nov 2025 04:22:32 UTC (479 KB)

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