5 citations · 17 across the 7 of their papers we have counts for
7 papers
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…
JAPAGEN: Efficient Few/Zero-shot Learning via Japanese Training Dataset Generation with LLM
Takuro Fujii, Satoru Katsumata
Recently some studies have highlighted the potential of Large Language Models (LLMs) as effective generators of supervised training data, offering advantages such as enhanced infer…
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…
Chinese Grammatical Correction Using BERT-based Pre-trained Model
Hongfei Wang, Michiki Kurosawa, Satoru Katsumata +1
In recent years, pre-trained models have been extensively studied, and several downstream tasks have benefited from their utilization. In this study, we verify the effectiveness of…
Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model
Satoru Katsumata, Mamoru Komachi
Studies on grammatical error correction (GEC) have reported the effectiveness of pretraining a Seq2Seq model with a large amount of pseudodata. However, this approach requires time…
Towards Unsupervised Grammatical Error Correction using Statistical Machine Translation with Synthetic Comparable Corpus
Satoru Katsumata, Mamoru Komachi
We introduce unsupervised techniques based on phrase-based statistical machine translation for grammatical error correction (GEC) trained on a pseudo learner corpus created by Goog…
Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models
Satoru Katsumata, Yukio Matsumura, Hayahide Yamagishi +1
Encoder-decoder models typically only employ words that are frequently used in the training corpus to reduce the computational costs and exclude noise. However, this vocabulary set…