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Constructing a Japanese Claim Decomposition Dataset for Fact-Checking of LLM-Generated Texts

Since texts generated by large language models (LLMs) may contain misinformation (hallucinations), develop- ing fact-checking systems capable of assessing their veracity has become increasingly important. One of the mainstream approaches to fact-checking is the claim-based one, which first decomposes a generated text into claims, i.e., independent and atomic units of information. Each claim is then used as a query to retrieve supporting evidence, and a verdict is predicted for each claim-evidence pair. Conducting fact-checking at the claim level enhances the explainability of verification results. However, achieving highly accurate verification requires that the text be decomposed into claims at an appropriate level of granularity. To address this, we constructed a dataset for Japanese claim decomposition. As part of this dataset construction, we design detailed guidelines for claim decomposition, ensuring that the extracted claims are in a form useful for fact-checking and that the decomposition rules mitigate annotator variability. Quantitative evaluation confirmed that the constructed dataset is of high quality. Additionally, experiments on prompt-based claim decomposition using the constructed dataset demonstrated that adding high-quality few-shot examples and guidelines to prompts improved performance.