A Notion of Complexity for Theory of Mind via Discrete World Models
Abstract: Theory of Mind (ToM) can be used to assess the capabilities of Large Language Models (LLMs) in complex scenarios where social reasoning is required.
While the research community has proposed many ToM benchmarks, their hardness varies greatly, and their complexity is not well defined.
This work proposes a framework to measure the complexity of ToM tasks.
We quantify a problem's complexity as the number of states necessary to solve it correctly.
Our complexity measure also accounts for spurious states of a ToM problem designed to make it apparently harder.
We use our method to assess the complexity of five widely adopted ToM benchmarks.
On top of this framework, we design a prompting technique that augments the information available to a model with a description of how the environment changes with the agents' interactions.
We name this technique Discrete World Models (DWM) and show how it elicits superior performance on ToM tasks.
Paper Type: Long
Research Area: Question Answering
Research Area Keywords: commonsense QA,logical reasoning,reasoning,conversational QA
Contribution Types: Model analysis & interpretability, Publicly available software and/or pre-trained models, Theory
Languages Studied: English
Submission Number: 2704
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