Boosting Theory-of-Mind Performance in Large Language Models via Prompting

TL;DR

This paper evaluates and enhances the Theory-of-Mind reasoning abilities of large language models like GPT-4 through prompting techniques, showing significant improvements with in-context learning and RLHF training.

Contribution

It systematically measures ToM performance of various LLMs and demonstrates that prompt engineering and RLHF training significantly boost their reasoning accuracy.

Findings

GPT-4 achieves nearly 80% ToM accuracy in zero-shot

In-context prompts improve RLHF-trained models to over 80% accuracy

GPT-4 reaches 100% accuracy with proper prompting

Abstract

Large language models (LLMs) excel in many tasks in 2023, but they still face challenges in complex reasoning. Theory-of-mind (ToM) tasks, which require understanding agents' beliefs, goals, and mental states, are essential for common-sense reasoning involving humans, making it crucial to enhance LLM performance in this area. This study measures the ToM performance of GPT-4 and three GPT-3.5 variants (Davinci-2, Davinci-3, GPT-3.5-Turbo), and investigates the effectiveness of in-context learning in improving their ToM comprehension. We evaluated prompts featuring two-shot chain of thought reasoning and step-by-step thinking instructions. We found that LLMs trained with Reinforcement Learning from Human Feedback (RLHF) (all models excluding Davinci-2) improved their ToM accuracy via in-context learning. GPT-4 performed best in zero-shot settings, reaching nearly 80% ToM accuracy, but still fell short of the 87% human accuracy on the test set. However, when supplied with prompts for in-context learning, all RLHF-trained LLMs exceeded 80% ToM accuracy, with GPT-4 reaching 100%. These results demonstrate that appropriate prompting enhances LLM ToM reasoning, and they underscore the context-dependent nature of LLM cognitive capacities.