LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments

arXiv:2402.16499v1 Announce Type: new
Abstract: Recent advancements in large language models (LLMs) have revealed their potential for achieving autonomous agents possessing human-level intelligence. However, existing benchmarks for evaluating LLM Agents either use static datasets, potentially leading to data leakage or focus only on single-agent scenarios, overlooking the complexities of multi-agent interactions. There is a lack of a benchmark that evaluates the diverse capabilities of LLM agents in multi-agent, dynamic environments. To this end, we introduce LLMArena, a novel and easily …

abstract agent agents arxiv autonomous autonomous agents benchmarks capabilities complexities cs.cl data data leakage datasets dynamic environments focus human intelligence language language models large language large language models llm llms multi-agent type