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View Atsuki Yamaguchi’s profile on LinkedIn, a professional community of 1 billion members. I am a PhD student at the University of Sheffield, UK. Before that, I was a full-time NLP…
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- Sheffield, England, United Kingdom
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Atsuki Yamaguchi. Proceedings of the 2021 Conference on Empirical Methods in Natural Language …. Proceedings of the 16th Conference of the European Chapter of the …. Proceedings of the First...
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Cross-lingual Transfer, Language Modelling, Natural Language Understanding, Natural Language Processing, Machine Learning
Cross-lingual transfer for efficient generative large language model inference The aim of this research is to explore inference-efficient methods for generative large language models under cross-li...Explore simple pretraining alternatives for Transformer-based language representaion models The aim of this research is to investigate simple yet effective pretraining objectives.Jun 17, 2024 · View a PDF of the paper titled How Can We Effectively Expand the Vocabulary of LLMs with 0.01GB of Target Language Text?, by Atsuki Yamaguchi and 2 other authors. Large language models (LLMs) have shown remarkable capabilities in many languages beyond English.
- arXiv:2406.11477 [cs.CL]
Atsuki Yamaguchi. Hitachi (Japan): Tokyo, JP. 2021-01-04 to 2022-03-31 | Associate Researcher (Research & Development Group) Employment. Show more detail. Source: Atsuki Yamaguchi. expand_more. Education and qualifications (2) sort Sort. University of Sheffield: Sheffield, GB.
Education. Sep 2023-Mar 2027 (expected) Sep 2019-Sep. Ph.D. in Computer Science, The University of Sheffield, Sheffield, United Kingdom. Research topic: Efficient cross-lingual transfer for generative large language models Advisors: Prof. Nikolaos Aletras and Prof. Aline Villavicencio.
Oct 4, 2024 · Hitachi at S em E val-2022 Task 2: On the Effectiveness of Span-based Classification Approaches for Multilingual Idiomaticity Detection. Atsuki Yamaguchi | Gaku Morio | Hiroaki Ozaki | Yasuhiro Sogawa. Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) pdf bib abs.
