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cs.CL2024★ 1 cited
Why We Build Local Large Language Models: An Observational Analysis from 35 Japanese and Multilingual LLMs
Koshiro Saito, Sakae Mizuki, Masanari Ohi +11
Why do we build local large language models (LLMs)? What should a local LLM learn from the target language? Which abilities can be transferred from other languages? Do language-spe…
cs.CL2024
Building a Japanese Document-Level Relation Extraction Dataset Assisted by Cross-Lingual Transfer
Youmi Ma, An Wang, Naoaki Okazaki
Document-level Relation Extraction (DocRE) is the task of extracting all semantic relationships from a document. While studies have been conducted on English DocRE, limited attenti…
cs.CL2024★ 2 cited
Sampling-based Pseudo-Likelihood for Membership Inference Attacks
Masahiro Kaneko, Youmi Ma, Yuki Wata +1
Large Language Models (LLMs) are trained on large-scale web data, which makes it difficult to grasp the contribution of each text. This poses the risk of leaking inappropriate data…