SmartPlay: A Benchmark for LLMs as Intelligent Agents
This provides a standardized testing ground for researchers and developers to assess LLM agents, though it is incremental as it builds on existing agent evaluation needs.
The authors tackled the lack of a systematic benchmark for evaluating large language models (LLMs) as intelligent agents by introducing SmartPlay, a benchmark with 6 games and up to 20 evaluation settings that tests 9 specific capabilities, such as reasoning and planning, to analyze performance gaps.
Recent large language models (LLMs) have demonstrated great potential toward intelligent agents and next-gen automation, but there currently lacks a systematic benchmark for evaluating LLMs' abilities as agents. We introduce SmartPlay: both a challenging benchmark and a methodology for evaluating LLMs as agents. SmartPlay consists of 6 different games, including Rock-Paper-Scissors, Tower of Hanoi, Minecraft. Each game features a unique setting, providing up to 20 evaluation settings and infinite environment variations. Each game in SmartPlay uniquely challenges a subset of 9 important capabilities of an intelligent LLM agent, including reasoning with object dependencies, planning ahead, spatial reasoning, learning from history, and understanding randomness. The distinction between the set of capabilities each game test allows us to analyze each capability separately. SmartPlay serves not only as a rigorous testing ground for evaluating the overall performance of LLM agents but also as a road-map for identifying gaps in current methodologies. We release our benchmark at github.com/Microsoft/SmartPlay