WiS Platform: Enhancing Evaluation of LLM-Based Multi-Agent Systems Through Game-Based Analysis

TL;DR

This paper introduces WiS, a comprehensive platform for evaluating LLM-based multi-agent systems through game-based analysis, enabling real-time, scalable, and detailed assessments of agent behaviors and strategies.

Contribution

The paper presents WiS, an open platform with a unified interface, real-time leaderboard, and extensive evaluation metrics for LLM-based MAS, addressing current evaluation challenges.

Findings

Different agents show distinct behaviors in the game.

WiS effectively evaluates LLM-based MAS performance.

Platform supports models from Hugging Face.

Abstract

Recent advancements in autonomous multi-agent systems (MAS) based on large language models (LLMs) have enhanced the application scenarios and improved the capability of LLMs to handle complex tasks. Despite demonstrating effectiveness, existing studies still evidently struggle to evaluate, analysis, and reproducibility of LLM-based MAS. In this paper, to facilitate the research on LLM-based MAS, we introduce an open, scalable, and real-time updated platform for accessing and analyzing the LLM-based MAS based on the games Who is Spy?" (WiS). Our platform is featured with three main worths: (1) a unified model evaluate interface that supports models available on Hugging Face; (2) real-time updated leaderboard for model evaluation; (3) a comprehensive evaluation covering game-winning rates, attacking, defense strategies, and reasoning of LLMs. To rigorously test WiS, we conduct extensive experiments coverage of various open- and closed-source LLMs, we find that different agents exhibit distinct and intriguing behaviors in the game. The experimental results demonstrate the effectiveness and efficiency of our platform in evaluating LLM-based MAS. Our platform and its documentation are publicly available at https://whoisspy.ai/.