LLM performance on naturalistic reasoning tasks tracks performance on equivalent Python code simulation, but the effect is partly driven by pattern matching and memorization rather than faithful execution.
A Notion of Complexity for Theory of Mind via Discrete World Models
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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 inspired by cognitive load theory 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.
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cs.LG 1years
2025 1verdicts
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Code Simulation as a Proxy for High-order Tasks in Large Language Models
LLM performance on naturalistic reasoning tasks tracks performance on equivalent Python code simulation, but the effect is partly driven by pattern matching and memorization rather than faithful execution.