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This work constructed a dataset for Japanese claim decomposition and designed 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.
This paper considers multilingual pretraining as a cooperative game in which each language acts as a player that jointly contributes to pretraining, gaining the resulting reduction in test loss as the payoff and proposes a game-theoretic multilingual scaling law called ShapleyLaw.
The neurofeedback paradigm for LLMs is redesigned so that the control target satisfies the privileged access requirement, which is closer to neurofeedback experiments in human cognitive neuroscience and indicates that rigorous assessments of metacognition in LLMs require evaluation methods that demand privileged access...
Large language models (LLMs) increasingly evaluate human writing in high-stakes domains such as hiring and academic assessment, putting non-native speakers at particular risk. Drawing on the language attitudes framework, we compared human and LLM evaluations of parallel L1- and L2-written Japanese emails on three dimen...
Evaluation results demonstrate that the synthetic dataset constructed is the most effective approach for improving LVLM performance on reading vertically written Japanese text.
This work proposes a measure of training data influence that does not require selecting a downstream task or validation set as the attribution target, and defines an example's influence by how much its gradient update reduces the squared distance to the final parameters of a given pretraining run.
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