Do Large Language Models Learn Human-Like Strategic Preferences?

Jesse Roberts; Kyle Moore; Doug Fisher

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arXiv · 2404.08710

Do Large Language Models Learn Human-Like Strategic Preferences?

Jesse Roberts·Kyle Moore·Doug Fisher

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Abstract

In this paper, we evaluate whether LLMs learn to make human-like preference judgements in strategic scenarios as compared with known empirical results. Solar and Mistral are shown to exhibit stable value-based preference consistent with humans and exhibit human-like preference for cooperation in the prisoner's dilemma (including stake-size effect) and traveler's dilemma (including penalty-size effect). We establish a relationship between model size, value-based preference, and superficiality. Finally, results here show that models tending to be less brittle have relied on sliding window attention suggesting a potential link. Additionally, we contribute a novel method for constructing preference relations from arbitrary LLMs and support for a hypothesis regarding human behavior in the traveler's dilemma.

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Jesse Roberts, Kyle Moore, Doug Fisher. 2024-04-11. Do Large Language Models Learn Human-Like Strategic Preferences?. https://arxiv.org/abs/2404.08710

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