Perceptions to Beliefs: Exploring Precursory Inferences for Theory of Mind in Large Language Models (2024)

Abstract: While humans naturally develop theory of mind (ToM), the capability to understand other people’s mental states and beliefs, state-of-the-art large language models (LLMs) underperform on simple ToM benchmarks. We posit that we can extend our understanding of LLMs’ ToM abilities by evaluating key human ToM precursors - perception inference and perception-to-belief inference - in LLMs. We introduce two datasets, Percept-ToMi and Percept-FANToM, to evaluate these precursory inferences for ToM in LLMs by annotating characters’ perceptions on ToMi and FANToM, respectively.Our evaluation of eight state-of-the-art LLMs reveals that the models generally perform well in perception inference while exhibiting limited capability in perception-to-belief inference (e.g., lack of inhibitory control).Based on these results, we present PercepToM, a novel ToM method leveraging LLMs’ strong perception inference capability while supplementing their limited perception-to-belief inference. Experimental results demonstrate that PercepToM significantly enhances LLM’s performance, especially in false belief scenarios.

## introduction humans interact with others in various social situations using theory of mind (tom), the cognitive capability to understand other's mental states (e.g., beliefs, desires, and thoughts; @xcite . while tom is naturally developed for humans in childhood, large language models (llms) are known to exhibit inconsistency in tom tasks @xcite . despite some early reports of successful cases @xcite , studies have shown that even state-of-the-art llms significantly lag behind human performance in tom tasks, particularly in false belief tests @xcite @xcite @xcite . ## augmenting perceptions on theory of mind benchmarks we construct perception-augmented theory of mind (tom) benchmarks to evaluate two essential cornerstones for tom in llms: (1) perception inference and (2) perception-to-belief inference capabilities. ## related work benchmarks for llm's theory of mind there has been a growing number of benchmarks aimed to evaluate llm's theory of mind (tom), including tomi @xcite , fantom @xcite , bigtom @xcite , hi-tom @xcite , tomchallenges @xcite , adv-csfb @xcite , and opentom @xcite . most of them adopt the false belief test @xcite , a famous psychology test developed to assess human tom capabilities. these benchmarks present scenarios involving a character who holds a false belief about a situation (e.g., not knowing something has changed). models are then asked to predict the character's thoughts or actions based on the false belief in the scenario. many benchmarks also include control scenarios where characters do not hold false belief (i.e., true belief scenarios) -situations where their belief about the world state matches the actual state @xcite @xcite . unlike existing benchmarks that primarily measure performance on (downstream) tom questions themselves, we aim to inquire into the underlying reasoning abilities of llm's theory of mind by examining the precursor of tom: the concept of seeing leads to knowing (baron-cohen and goodhart, 1994; @xcite . we expand existing datasets to identify the perception inference and perception-to-belief inference capabilities, which are essential for tom reasoning. ## conclusion inspired by the psychology literature, we evaluated the precursory inferences for human theory of mind (tom) in large language models (llm), aiming to broaden our insight into their tom capabilities. to this end, we constructed perceptionaugmented tom benchmarks, percept-tomi and percept-fantom, by annotating character perceptions about the contexts. through evaluations and analyses of eight state-of-the-art llms, we found that they perform reasonably well in inferring others' perceptions but struggle with inferring others' beliefs based on that perceptual information. based on these findings, we proposed a new framework, perceptom, to improve llm's tom reasoning. our framework leverages llms' strength in perception inference and enhances their perception-tobelief inference by extracting the relevant contexts. we expect our work to provide insights and enable further in-depth studies into the extent of llms' tom capabilities and targeted improvements in their weaknesses.