Semantic Structure-Mapping in LLM and Human Analogical Reasoning

Analogical reasoning is considered core to human learning and cognition. Recent studies have compared the analogical reasoning abilities of human subjects and Large Language Models (LLMs) on abstract symbol manipulation tasks, such as letter string analogies. However, these studies largely neglect analogical reasoning over semantically meaningful symbols, such as natural language words. This ability to draw analogies that link language to non-linguistic domains, which we term semantic structure-mapping, is thought to play a crucial role in language acquisition and broader cognitive development. We test human subjects and LLMs on analogical reasoning tasks that require the transfer of semantic structure and content from one domain to another. Advanced LLMs match human performance across many task variations. However, humans and LLMs respond differently to certain task variations and semantic distractors. Overall, our data suggest that LLMs are approaching human-level performance on these important cognitive tasks, but are not yet entirely human like.

Testing Causal Models of Word Meaning in GPT-3 and -4

Large Language Models (LLMs) have driven extraordinary improvements in NLP. However, it is unclear how such models represent lexical concepts-i.e., the meanings of the words they use. This paper evaluates the lexical representations of GPT-3 and GPT-4 through the lens of HIPE theory, a theory of concept representations which focuses on representations of words describing artifacts (such as "mop", "pencil", and "whistle"). The theory posits a causal graph that relates the meanings of such words to the form, use, and history of the objects to which they refer. We test LLMs using the same stimuli originally used by Chaigneau et al. (2004) to evaluate the theory in humans, and consider a variety of prompt designs. Our experiments concern judgements about causal outcomes, object function, and object naming. We find no evidence that GPT-3 encodes the causal structure hypothesized by HIPE, but do find evidence that GPT-4 encodes such structure. The results contribute to a growing body of research characterizing the representational capacity of large language models.