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.
Demystifying Mixed Outcomes of Self-Training: Pre-training Analyses on Non-Toy LLMs (2026)
Studies of self-training disagree. Some report that LLMs improve when retrained on their own outputs, while others report model collapse. The authors suspect the disagreement comes from different experimental setups: strong models with engineered pipelines on one side, and toy models with unconditional generation on…

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Updated 1 November 2026