5 citations · 5 across the 6 of their papers we have counts for
9 papers · 1 filter
Rethinking the Evaluation of LLM Fact-Checkers
Ayana Niwa, Miyu Yamada, Kouta Nakayama +3
Large language models (LLMs) are widely used to answer factual questions, but their answers can contain errors and must be fact-checked claim by claim. Yet the resulting score depe…
LLM-jp-4 Technical Report
LLM-jp, :, Chaoran Liu +16
This paper presents LLM-jp-4, a family of Japanese sovereign large language models (LLMs) developed with an emphasis on transparency, reproducibility, and practical usability. LLM-…
How Well Can LLMs Simulate Real Learner Evaluations of Educational Feedback?
Momoka Furuhashi, Kouta Nakayama, Takashi Kodama +2
While recent studies have explored human behavior and preference simulation using large language models (LLMs), it remains unclear how well LLMs can simulate subjective evaluations…
Exclusive Unlearning
Mutsumi Sasaki, Kouta Nakayama, Yusuke Miyao +2
When introducing Large Language Models (LLMs) into industrial applications, such as healthcare and education, the risk of generating harmful content becomes a significant challenge…
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 coverage) to support answ…
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…
llm-jp-modernbert: A ModernBERT Model Trained on a Large-Scale Japanese Corpus with Long Context Length
Issa Sugiura, Kouta Nakayama, Yusuke Oda
Encoder-only transformer models like BERT are widely adopted as a pre-trained backbone for tasks like sentence classification and retrieval. However, pretraining of encoder models…
LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs
LLM-jp, :, Akiko Aizawa +80
This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs). LLM-jp aims to develop open-source and stron…