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AgentSims: An Open-Source Sandbox for Large Language Model Evaluation

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arxiv 2308.04026 v1 pith:ERIKW5NU submitted 2023-08-08 cs.AI

AgentSims: An Open-Source Sandbox for Large Language Model Evaluation

classification cs.AI
keywords evaluationagentsimsagentslanguagelargeresearcherstaskstest
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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With ChatGPT-like large language models (LLM) prevailing in the community, how to evaluate the ability of LLMs is an open question. Existing evaluation methods suffer from following shortcomings: (1) constrained evaluation abilities, (2) vulnerable benchmarks, (3) unobjective metrics. We suggest that task-based evaluation, where LLM agents complete tasks in a simulated environment, is a one-for-all solution to solve above problems. We present AgentSims, an easy-to-use infrastructure for researchers from all disciplines to test the specific capacities they are interested in. Researchers can build their evaluation tasks by adding agents and buildings on an interactive GUI or deploy and test new support mechanisms, i.e. memory, planning and tool-use systems, by a few lines of codes. Our demo is available at https://agentsims.com .

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Cited by 15 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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