GameBench: Evaluating Strategic Reasoning Abilities of LLM Agents

• URL: http://arxiv.org/abs/2406.06613v2
• Date: Mon, 22 Jul 2024 14:32:33 GMT
• Title: GameBench: Evaluating Strategic Reasoning Abilities of LLM Agents
• Authors: Anthony Costarelli, Mat Allen, Roman Hauksson, Grace Sodunke, Suhas Hariharan, Carlson Cheng, Wenjie Li, Joshua Clymer, Arjun Yadav,
• Abstract summary: We introduce GameBench, a cross-domain benchmark for evaluating strategic reasoning abilities of large language models. Our evaluations use GPT-3 and GPT-4 in their base form along with two scaffolding frameworks designed to enhance strategic reasoning ability: Chain-of-Thought (CoT) prompting and Reasoning Via Planning (RAP) Our results show that none of the tested models match human performance, and at worst GPT-4 performs worse than random action.
• Score: 4.209869303518743
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models have demonstrated remarkable few-shot performance on many natural language understanding tasks. Despite several demonstrations of using large language models in complex, strategic scenarios, there lacks a comprehensive framework for evaluating agents' performance across various types of reasoning found in games. To address this gap, we introduce GameBench, a cross-domain benchmark for evaluating strategic reasoning abilities of LLM agents. We focus on 9 different game environments, where each covers at least one axis of key reasoning skill identified in strategy games, and select games for which strategy explanations are unlikely to form a significant portion of models' pretraining corpuses. Our evaluations use GPT-3 and GPT-4 in their base form along with two scaffolding frameworks designed to enhance strategic reasoning ability: Chain-of-Thought (CoT) prompting and Reasoning Via Planning (RAP). Our results show that none of the tested models match human performance, and at worst GPT-4 performs worse than random action. CoT and RAP both improve scores but not comparable to human levels.

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