Recent work has proposed a methodology for the systematic evaluation of "Situated Language Understanding Agents"-agents that operate in rich linguistic and non-linguistic contexts-through testing them in carefully constructed interactive settings. Other recent work has argued that Large Language Models (LLMs), if suitably set up, can be understood as (simulators of) such agents. A connection suggests itself, which this paper explores: Can LLMs be evaluated meaningfully by exposing them to constrained game-like settings that are built to challenge specific capabilities? As a proof of concept, this paper investigates five interaction settings, showing that current chat-optimised LLMs are, to an extent, capable to follow game-play instructions. Both this capability and the quality of the game play, measured by how well the objectives of the different games are met, follows the development cycle, with newer models performing better. The metrics even for the comparatively simple example games are far from being saturated, suggesting that the proposed instrument will remain to have diagnostic value. Our general framework for implementing and evaluating games with LLMs is available at https://github.com/clembench .

翻译：近期研究提出了一种通过精心构建的交互式测试环境，系统评估“情境化语言理解智能体”——即在丰富语言及非语言情境中运作的智能体——的方法论。另有研究认为，若设置得当，大型语言模型（LLM）可被视为这类智能体（或其模拟器）。两者间的潜在关联促使本文展开探索：能否通过让LLM暴露于针对特定能力设计的受限类游戏场景中，对其性能进行有意义的评估？作为概念验证，本文研究了五种交互场景，结果表明：当前面向对话优化的LLM在一定程度上能遵循游戏玩法指令。这一能力以及游戏质量（以各游戏目标的达成度衡量）均随模型迭代周期提升，新模型表现更优。即使是相对简单的示例游戏，其评估指标也远未饱和，表明所提出的评估工具将持续具有诊断价值。我们用于实施和评估LLM游戏的通用框架已发布于 https://github.com/clembench。