17 citations · 55 across the 15 of their papers we have counts for
15 papers
Exploring the Effects of Alignment on Numerical Bias in Large Language Models
Ayako Sato, Hwichan Kim, Zhousi Chen +2
"LLM-as-a-judge," which utilizes large language models (LLMs) as evaluators, has proven effective in many evaluation tasks. However, evaluator LLMs exhibit numerical bias, a phenom…
Developmentally-plausible Working Memory Shapes a Critical Period for Language Acquisition
Masato Mita, Ryo Yoshida, Yohei Oseki
Large language models possess general linguistic abilities but acquire language less efficiently than humans. This study proposes a method for integrating the developmental charact…
Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction
Masamune Kobayashi, Masato Mita, Mamoru Komachi
Large Language Models (LLMs) have been reported to outperform existing automatic evaluation metrics in some tasks, such as text summarization and machine translation. However, ther…
Revisiting Meta-evaluation for Grammatical Error Correction
Masamune Kobayashi, Masato Mita, Mamoru Komachi
Metrics are the foundation for automatic evaluation in grammatical error correction (GEC), with their evaluation of the metrics (meta-evaluation) relying on their correlation with…
Japanese Lexical Complexity for Non-Native Readers: A New Dataset
Yusuke Ide, Masato Mita, Adam Nohejl +2
Lexical complexity prediction (LCP) is the task of predicting the complexity of words in a text on a continuous scale. It plays a vital role in simplifying or annotating complex wo…
Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond
Masato Mita, Keisuke Sakaguchi, Masato Hagiwara +3
Natural language processing technology has rapidly improved automated grammatical error correction tasks, and the community begins to explore document-level revision as one of the…