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

• URL: http://arxiv.org/abs/2407.06004v3
• Date: Wed, 6 Nov 2024 22:07:06 GMT
• Title: Perceptions to Beliefs: Exploring Precursory Inferences for Theory of Mind in Large Language Models
• Authors: Chani Jung, Dongkwan Kim, Jiho Jin, Jiseon Kim, Yeon Seonwoo, Yejin Choi, Alice Oh, Hyunwoo Kim,
• Abstract summary: We evaluate key human ToM precursors by annotating characters' perceptions on ToMi and FANToM. We present PercepToM, a novel ToM method leveraging LLMs' strong perception inference capability while supplementing their limited perception-to-belief inference.
• Score: 51.91448005607405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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