LLMArena: Assessing Capabilities of Large Language Models in Dynamic
  Multi-Agent Environments
Junzhe Chen Xuming Hu Shuodi Liu Shiyu Huang Wei-Wei Tu Zhaofeng He Lijie Wen
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 extensible framework for evaluating the diverse capabilities of LLM
in multi-agent dynamic environments. LLMArena encompasses seven distinct gaming
environments, employing Trueskill scoring to assess crucial abilities in LLM
agents, including spatial reasoning, strategic planning, numerical reasoning,
risk assessment, communication, opponent modeling, and team collaboration. We
conduct an extensive experiment and human evaluation among different sizes and
types of LLMs, showing that LLMs still have a significant journey ahead in
their development towards becoming fully autonomous agents, especially in
opponent modeling and team collaboration. We hope LLMArena could guide future
research towards enhancing these capabilities in LLMs, ultimately leading to
more sophisticated and practical applications in dynamic, multi-agent settings.
The code and data will be available.