Do Large Language Models Learn Human-Like Strategic Preferences?

• URL: http://arxiv.org/abs/2404.08710v2
• Date: Wed, 02 Oct 2024 17:54:17 GMT
• Title: Do Large Language Models Learn Human-Like Strategic Preferences?
• Authors: Jesse Roberts, Kyle Moore, Doug Fisher,
• Abstract summary: LLMs learn to make human-like preference judgements in strategic scenarios. Solar and Mistral are shown to exhibit stable value-based preference consistent with humans.
• Score: 0.0
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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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