Takashi Kodama

Generation of Instruction and Preference Dataset for Improving Japanese Instruction Following in LLMs

AnswerCarefully: Creating a Dataset for LLM Safety in Japanese

Investigating Learner-Aware Design of LLM-Generated Educational Feedback

Presenting task-relevant information clearly is associated with better immediate revision performance and is favorably evaluated across learner profiles, while descriptive differences in evaluation patterns are observed, particularly for informational novelty and affective framing.

How Well Can LLMs Simulate Real Learner Evaluations of Educational Feedback?

It is shown that LLMs still have a limited ability to simulate learner evaluations, and the need to investigate which learner information and adaptation strategies are effective for learner preference simulation is highlighted.

Measuring Task-Agnostic Training Data Influence Across Language Model Pretraining

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.