MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic

TL;DR

This paper introduces a novel approach using dynamic epistemic logic to evaluate Theory of Mind in large language models, revealing that scaling alone does not guarantee improved reasoning, with GPT-4 showing some capabilities but still room for growth.

Contribution

It presents a new framework for assessing ToM in language models through controlled problems and verbalization techniques based on dynamic epistemic logic.

Findings

Scaling models does not consistently improve ToM performance.

GPT-4 shows better epistemic reasoning than smaller models.

Room for improvement remains in language models' ToM abilities.

Abstract

Theory of Mind (ToM) is a critical component of intelligence but its assessment remains the subject of heated debates. Prior research applied human ToM assessments to natural language processing models using either human-created standardized tests or rule-based templates. However, these methods primarily focus on simplistic reasoning and require further validation. Here, we leverage dynamic epistemic logic to isolate a particular component of ToM and to generate controlled problems. We also introduce new verbalization techniques to express these problems in English natural language. Our findings indicate that some language model scaling (from 70M to 6B and 350M to 174B) does not consistently yield results better than random chance. While GPT-4 demonstrates superior epistemic reasoning capabilities, there is still room for improvement. Our code and datasets are publicly available (https://huggingface.co/datasets/sileod/mindgames , https://github.com/sileod/llm-theory-of-mind )