Strategic behavior of large language models and the role of game structure versus contextual framing.

ABSTRACT: This paper investigates the strategic behavior of large language models (LLMs) across various game-theoretic settings, scrutinizing the interplay between game structure and contextual framing in decision-making. We focus our analysis on three advanced LLMs-GPT-3.5, GPT-4, and LLaMa-2-and how they navigate both the intrinsic aspects of different games and the nuances of their surrounding contexts. Our results highlight discernible patterns in each model's strategic approach. GPT-3.5 shows significant sensitivity to context but lags in its capacity for abstract strategic decision making. Conversely, both GPT-4 and LLaMa-2 demonstrate a more balanced sensitivity to game structures and contexts, albeit with crucial differences. Specifically, GPT-4 prioritizes the internal mechanics of the game

SUBMITTER: Lore N 

PROVIDER: S-EPMC11316122 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature