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

Understand

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.