使用修辞学与LLM进行语义变化表征
Semantic Change Characterization with LLMs using Rhetorics
AI总结：
本文研究LLM在表征维度、关系和方向三种语义变化中的潜力，通过将Chain-of-Thought与修辞手法结合并使用新数据集评估，证明了该方法能有效捕捉和分析语义变化，为改进计算语言学应用提供见解。
摘要：
语言不断演变以响应社会事件，导致新术语产生和意义转变。这些变化对自动翻译和聊天机器人等计算机应用具有重大影响，因此准确表征它们至关重要。近期LLM的发展显著推进了自然语言理解，特别是在语义推断和推理方面。本文研究了LLM在表征三种语义变化类型中的潜力：维度、关系和方向。我们通过将LLM的Chain-of-Thought与修辞手法相结合，并使用新创建的数据集对我们的方法进行了实验评估。结果突出了LLM在捕捉和分析语义变化方面的有效性，为改进计算语言学应用提供了宝贵见解。
英文摘要：
Languages continually evolve in response to societal events, resulting in new terms and shifts in meanings. These changes have significant implications for computer applications, including automatic translation and chatbots, making it essential to characterize them accurately. The recent development of LLMs has notably advanced natural language understanding, particularly in sense inference and reasoning. In this paper, we investigate the potential of LLMs in characterizing three types of semantic change: dimension, relation, and orientation. We achieve this by combining LLMs' Chain-of-Thought with rhetorical devices and conducting an experimental assessment of our approach using newly created datasets. Our results highlight the effectiveness of LLMs in capturing and analyzing semantic changes, providing valuable insights to improve computational linguistic applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
