Constructing a Japanese Claim Decomposition Dataset for Fact-Checking of LLM-Generated Texts (2026 · 1 citation)
LLM outputs can contain hallucinations. A common way to fact-check them is to first split the text into atomic, self-contained claims, then retrieve evidence for each claim and predict a verdict. How well the text is split strongly affects the final checking accuracy. Earlier resources mostly had annotators review…
Constructing a Japanese Verdict Prediction Dataset for Fact-Checking of LLM-Generated Texts (2026)
Fact-checking pipelines for LLM output usually end with verdict prediction, in which a claim is compared against retrieved evidence.
Detecting Sensitive Personal Information in Japanese Pre-Training Corpora for Large Language Models (2026)
The paper tackles a gap in Japanese LLM data curation: web-crawled pre-training corpora may contain sensitive personal data that Japan's APPI classifies as special care-required personal information (SCPI), yet no Japanese work had studied detecting it, and legal restrictions mean no public SCPI dataset exists.

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Updated 1 November 2026