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

TL;DR

This study evaluates the effectiveness of large language models in political fact-checking, finding that curated high-quality context significantly improves accuracy over standard reasoning and web search methods.

Contribution

The paper demonstrates that curated, high-quality context greatly enhances LLM-based fact-checking performance, surpassing reasoning and web search approaches.

Findings

Standard LLMs perform poorly on fact-checking tasks.

Reasoning capabilities offer minimal improvements.

Web search provides only moderate gains.

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.