Evaluating the Effect of Sub-Word Training on Syntactic Language Models -A Linguistic Perspective-

• Item type: National Convention(1)
• 公開日: 2024-03-01
• タイトル: Evaluating the Effect of Sub-Word Training on Syntactic Language Models -A Linguistic Perspective-
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: 人工知能と認知科学
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_5794
• 資源タイプ: conference paper
• 著者所属: 筑波大
• 著者名: 岡村, 康平
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: This study evaluates the effect of incorporating sub-word units (morphemes) in training large language models (LLMs), highlighting its role in reducing vocabulary size and managing unknown and compound words more effectively. Beyond these established benefits, the research also emphasises the importance of considering the hierarchical morphological structures of languages and their generative capacities. It addresses how simplistic assumptions underpinning syntactic competence, which focus solely on surface linear strings, struggle to resolve linguistic problems such as ambiguities (e.g., un-lockable versus unlock-able) and long-dependencies (e.g., en-joy-able versus *joy-able). The research posits that an improved understanding of morphological structures, grounded in the Hierarchical Morphological Processing Hypothesis, can enhance the performance and accuracy of LLMs.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00349328
• 書誌情報: 第86回全国大会講演論文集; 巻 2024, 号 1, p. 867-868, 発行日 2024-03-01
• 出版者 / 言語: ja
• 出版者: 情報処理学会