LLMs能否像人类一样解读隐喻语言？：表层相似性与表征相似性
Can LLMs interpret figurative language as humans do?: surface-level vs representational similarity
AI总结：
本文研究了LLM在解读隐喻性语言方面是否能像人类一样，发现GPT-4在表征层面最接近人类，但所有模型在处理讽刺、俚语和隐喻时均表现不佳。
摘要：
大型语言模型生成的判断与人类相似。然而，这些模型在解读隐喻性和社会性语言方面与人类判断的契合程度仍不确定。为此，人类参与者和四个不同大小的指令微调LLM（GPT-4、Gemma-2-9B、Llama-3.2和Mistral-7B）对240个基于对话的句子进行了评分，这些句子代表了六个语言特征：常规性、讽刺、有趣、情感、隐喻性和俚语。每句240个句子都配对了40个解释性问题，并由人类和LLM在10分量表上对这些句子进行评分。结果显示，人类和LLM在表层上与人类一致，但在表征层面则显著分歧，尤其是在解读涉及隐喻和Z世代俚语的句子时。GPT-4最接近人类的表征模式，而所有模型在解读依赖上下文和社会语用表达如讽刺、俚语和隐喻时都存在困难。
英文摘要：
Large language models generate judgments that resemble those of humans. Yet the extent to which these models align with human judgments in interpreting figurative and socially grounded language remains uncertain. To investigate this, human participants and four instruction-tuned LLMs of different sizes (GPT-4, Gemma-2-9B, Llama-3.2, and Mistral-7B) rated 240 dialogue-based sentences representing six linguistic traits: conventionality, sarcasm, funny, emotional, idiomacy, and slang. Each of the 240 sentences was paired with 40 interpretive questions, and both humans and LLMs rated these sentences on a 10-point Likert scale. Results indicated that humans and LLMs aligned at the surface level with humans, but diverged significantly at the representational level, especially in interpreting figurative sentences involving idioms and Gen Z slang. GPT-4 most closely approximates human representational patterns, while all models struggle with context-dependent and socio-pragmatic expressions like sarcasm, slang, and idiomacy.
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。
