[2603.05471] Leveraging LLM Parametric Knowledge for Fact Checking without Retrieval

Computer Science > Computation and Language arXiv:2603.05471 (cs) [Submitted on 5 Mar 2026] Title:Leveraging LLM Parametric Knowledge for Fact Checking without Retrieval Authors:Artem Vazhentsev, Maria Marina, Daniil Moskovskiy, Sergey Pletenev, Mikhail Seleznyov, Mikhail Salnikov, Elena Tutubalina, Vasily Konovalov, Irina Nikishina, Alexander Panchenko, Viktor Moskvoretskii View a PDF of the paper titled Leveraging LLM Parametric Knowledge for Fact Checking without Retrieval, by Artem Vazhentsev and 10 other authors View PDF HTML (experimental) Abstract:Trustworthiness is a core research challenge for agentic AI systems built on Large Language Models (LLMs). To enhance trust, natural language claims from diverse sources, including human-written text, web content, and model outputs, are commonly checked for factuality by retrieving external knowledge and using an LLM to verify the faithfulness of claims to the retrieved evidence. As a result, such methods are constrained by retrieval errors and external data availability, while leaving the models intrinsic fact-verification capabilities largely unused. We propose the task of fact-checking without retrieval, focusing on the verification of arbitrary natural language claims, independent of their source. To study this setting, we introduce a comprehensive evaluation framework focused on generalization, testing robustness to (i) long-tail knowledge, (ii) variation in claim sources, (iii) multilinguality, and (iv) long-form gen...