标题： 桌游竞技场：通过战略博弈评估大型语言模型的框架和基准 显示英文标题

标题： Board Game Arena: A Framework and Benchmark for Assessing Large Language Models via Strategic Play

摘要： 该Board Game Arena库提供了一个框架，通过Google OpenSpiel库中实现的战略棋盘游戏来评估大型语言模型（LLMs）的决策能力。 该框架通过包装多种棋盘和矩阵游戏并支持不同的代理类型，使LLM代理与其他代理（随机、人类、强化学习代理等）在各种游戏场景中进行系统比较。 它通过LiteLLM集成对模型的API访问，通过vLLM实现本地模型部署，并通过Ray提供分布式执行。 此外，它还提供了用于LLM推理轨迹的广泛分析工具。 本文总结了该仓库的结构、关键特征和动机，强调了它如何有助于LLM推理和博弈论行为的实证评估。 显示英文摘要

摘要： The Board Game Arena library provides a framework for evaluating the decision making abilities of large language models (LLMs) through strategic board games implemented in Google OpenSpiel library. The framework enables systematic comparisons between LLM based agents and other agents (random, human, reinforcement learning agents, etc.) in various game scenarios by wrapping multiple board and matrix games and supporting different agent types. It integrates API access to models via LiteLLM, local model deployment via vLLM, and offers distributed execution through Ray. Additionally it provides extensive analysis tools for the LLM reasoning traces. This paper summarizes the structure, key characteristics, and motivation of the repository, highlighting how it contributes to the empirical evaluation of the reasoning of LLM and game-theoretic behavior