4 papers
On the Structural Limits of Machine Learning Decision Systems: An Information-Theoretic, Interaction-Based, and Stochastic-Dynamical Perspective
Nestor R. Barraza, Gabriel Pena
Machine learning procedures are commonly evaluated in terms of predictive accuracy and computational efficiency. However, their achievable performance is fundamentally constrained…
Investigating Learner-Aware Design of LLM-Generated Educational Feedback
Momoka Furuhashi, Kouta Nakayama, Noboru Kawai +3
Although large language models (LLMs) show promise for generating educational feedback, it remains unclear how feedback should be designed (e.g., tone and information coverage) to…
Are Checklists Really Useful for Automatic Evaluation of Generative Tasks?
Momoka Furuhashi, Kouta Nakayama, Takashi Kodama +1
Automatic evaluation of generative tasks using large language models faces challenges due to ambiguous criteria. Although automatic checklist generation is a potentially promising…
AnswerCarefully: A Dataset for Improving the Safety of Japanese LLM Output
Hisami Suzuki, Satoru Katsumata, Takashi Kodama +3
In this paper we present AnswerCarefully, a dataset for promoting the safety and appropriateness of Japanese LLM outputs. The dataset consists of 1,800 pairs of questions and refer…