Reasoning about Affordances: Causal and Compositional Reasoning in LLMs

With the rapid progress of Large Language Models (LLMs), it becomes increasingly important to understand their abilities and limitations. In two experiments, we investigate the causal and compositional reasoning abilities of LLMs and humans in the domain of object affordances, an area traditionally linked to embodied cognition. The tasks, designed from scratch to avoid data contamination, require decision-makers to select unconventional objects to replace a typical tool for a particular purpose, such as using a table tennis racket to dig a hole. In Experiment 1, we evaluated GPT-3.5 and GPT-4o, finding that GPT-4o, when given chain-of-thought prompting, performed on par with human participants, while GPT-3.5 lagged significantly. In Experiment 2, we introduced two new conditions, Distractor (more object choices, increasing difficulty) and Image (object options presented visually), and evaluated Claude 3 Sonnet and Claude 3.5 Sonnet in addition to the GPT models. The Distractor condition significantly impaired performance across humans and models, although GPT-4o and Claude 3.5 still performed well above chance. Surprisingly, the Image condition had little impact on humans or GPT-4o, but significantly lowered Claude 3.5's accuracy. Qualitative analysis showed that GPT-4o and Claude 3.5 have a stronger ability than their predecessors to identify and flexibly apply causally relevant object properties. The improvement from GPT-3.5 and Claude 3 to GPT-4o and Claude 3.5 suggests that models are increasingly capable of causal and compositional reasoning in some domains, although further mechanistic research is necessary to understand how LLMs reason.