基于贪心搜索的链式推理提示的俚语推理增强
Slang Context-based Inference Enhancement via Greedy Search-Guided Chain-of-Thought Prompting
AI总结：
本文提出一种基于贪心搜索的链式推理框架，通过改进小语言模型的推理能力，提升俚语解释的准确性。
摘要：
俚语解释一直是大型语言模型（LLMs）的挑战性下游任务，因为这些表达本质上嵌入在语境、文化和语言框架中。在缺乏领域特定训练数据的情况下，LLMs难以根据词汇信息准确解释俚语含义。本文尝试研究大型LLMs在俚语推理中的挑战，并提出一种贪心搜索引导的链式推理框架用于俚语解释。通过实验我们得出结论，模型大小和温度设置对推理准确性影响有限。基于参数较多的Transformer模型并不比参数较少的模型生成更高的准确性。基于上述经验研究结果，我们整合贪心搜索算法与链式推理提示，为小型语言模型构建一个提升俚语解释准确性的框架。实验结果表明，所提出的框架在俚语含义解释上表现出更高的准确性。这些发现有助于理解语言模型中的上下文依赖性，并为通过结构化推理提示框架增强俚语理解提供了实用解决方案。
英文摘要：
Slang interpretation has been a challenging downstream task for Large Language Models (LLMs) as the expressions are inherently embedded in contextual, cultural, and linguistic frameworks. In the absence of domain-specific training data, it is difficult for LLMs to accurately interpret slang meaning based on lexical information. This paper attempts to investigate the challenges of slang inference using large LLMs and presents a greedy search-guided chain-of-thought framework for slang interpretation. Through our experiments, we conclude that the model size and temperature settings have limited impact on inference accuracy. Transformer-based models with larger active parameters do not generate higher accuracy than smaller models. Based on the results of the above empirical study, we integrate greedy search algorithms with chain-of-thought prompting for small language models to build a framework that improves the accuracy of slang interpretation. The experimental results indicate that our proposed framework demonstrates improved accuracy in slang meaning interpretation. These findings contribute to the understanding of context dependency in language models and provide a practical solution for enhancing slang comprehension through a structured reasoning prompting framework.
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。
