Debatrix: Multi-dimensinal Debate Judge with Iterative Chronological
  Analysis Based on LLM
Jingcong Liang Rong Ye Meng Han Ruofei Lai Xinyu Zhang Xuanjing Huang Zhongyu Wei
How can we construct an automated debate judge to evaluate an extensive,
vibrant, multi-turn debate? This task is challenging, as judging a debate
involves grappling with lengthy texts, intricate argument relationships, and
multi-dimensional assessments. At the same time, current research mainly
focuses on short dialogues, rarely touching upon the evaluation of an entire
debate. In this paper, by leveraging Large Language Models (LLMs), we propose
Debatrix, which makes the analysis and assessment of multi-turn debates more
aligned with majority preferences. Specifically, Debatrix features a vertical,
iterative chronological analysis and a horizontal, multi-dimensional evaluation
collaboration. To align with real-world debate scenarios, we introduced the
PanelBench benchmark, comparing our system's performance to actual debate
outcomes. The findings indicate a notable enhancement over directly using LLMs
for debate evaluation. Source code and benchmark data are available online at
https://github.com/ljcleo/Debatrix .