GPT-4生成的拟音词能被人类和模型准确猜出含义，显示大模型具备隐含象似性表征能力。
Iconicity in Large Language Models
- 用GPT-4生成具有声音-意义关联的虚构语言伪词
- 人类对伪词语义猜测准确率达67.2%，模型更高
- 揭示大模型可能内化跨模态象似性机制，适合认知科学与NLP研究者
词汇象似性（即词语形式与意义之间的直接关联）是自然语言的重要特征，通常体现为声音-意义的对应。由于大语言模型（LLMs）对意义的获取依赖文本上下文，对声音的感知则通过书面形式间接实现，且受分词影响，我们预期其编码象似性的能力可能不足或与人类不同。本研究通过GPT-4生成具有高度象似性的虚构语言伪词，并邀请捷克和德国参与者（n=672）进行语义猜测，随后由GPT-4和Claude 3.5 Sonnet生成的模型参与者重复测试。结果表明，人类对伪词的语义猜测准确率显著高于远源自然语言，而基于模型的参与者表现甚至优于人类。该核心发现伴随多项分析，涉及生成语言的普适性以及人类与模型参与者所依赖的线索。
原文摘要 · Abstract (English)
Lexical iconicity, a direct relation between a word's meaning and its form, is an important aspect of every natural language, most commonly manifesting through sound-meaning associations. Since Large language models' (LLMs') access to both meaning and sound of text is only mediated (meaning through textual context, sound through written representation, further complicated by tokenization), we might expect that the encoding of iconicity in LLMs would be either insufficient or significantly different from human processing. This study addresses this hypothesis by having GPT-4 generate highly iconic pseudowords in artificial languages. To verify that these words actually carry iconicity, we had their meanings guessed by Czech and German participants (n=672) and subsequently by LLM-based participants (generated by GPT-4 and Claude 3.5 Sonnet). The results revealed that humans can guess the meanings of pseudowords in the generated iconic language more accurately than words in distant natural languages and that LLM-based participants are even more successful than humans in this task. This core finding is accompanied by several additional analyses concerning the universality of the generated language and the cues that both human and LLM-based participants utilize.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。