Note: This work is a close collaboration between myself and Nathan Labenz (@Nathan). Many thanks to him for starting this particular piece of research and letting me tag along as well to @JustisMills for help on navigating the submission process and giving feedback on the draft. If you're interested in running experiments yourself, check out our Replit instance or read the code on github.

Introduction

Theory of mind (ToM) is the ability to attribute mental states to ourselves and others; accurately predicting other people’s beliefs, intents, desires and emotions is key to navigating everyday life. Understanding whether LLMs have theory of mind is an interesting theoretical question. However the question of whether AI can accurately assess human state of mind has important practical implications too. Deception is a key component to many of the scenarios that concern the AI safety community, and theory of mind is a key component of deception.

In addition to being an important question, identifying the extent of ToM in LLMs provides a rich canvas against which to discuss capability evaluation in general. We’ve tried to include key takeaways in each section that are practical considerations when trying to test whether LLMs have a given capability. Each task is unique, but we think we’ve found some important things to consider, if you want to understand AI ability in both best case and worst case scenarios.

We begin by discussing the particular problem and data set we are trying to evaluate, TOMI. We evaluate GPT-4’s capability to answer these questions correctly using a prompt with more context and a chain of thought approach, and compare this approach to prior results. We then classify the errors the model made into several buckets and examine the patterns. Then, we discuss adjustments to the prompts that have interesting implications on the model’s performance. Finally, we discuss the variability of same-prompt output we observed using the GPT-4 API at temperature 0 and how that impacts our ability to measure the model’s capabilities.

The TOMI Dataset; zero shot vs. chain of thought performance

The TOMI data set, which comes from this Le et. al. paper in 2019, is a set of stories and questions that try to measure the answerer’s ability to understand the state of mind of different characters in each story. The questions are structured with two parts: a story (set of ...

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