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 .

Jessica Melugin, Competitive Enterprise Institute Director of Center for Technology and Innovation, discusses Twitter accusing Meta of stealing trade secrets and a New York City law requiring businesses to audit A.I. hiring tools. Former Google CEO Eric Schmidt recently re-upped his calls for a global body, akin to the Intergovernmental Panel on Climate Change (IPCC), to advise member nations on regulating artificial intelligence (AI). Schmidt first made his case for an "International Panel on AI Safety" – an "IPCC for AI," if you will – in an October 2023 op-ed in the Financial Times. He writes of the AI panel's potential to be an, "an independent, expert-led body empowered to objectively inform governments about the current state of AI capabilities and make evidence-based predictions." He claims that AI policy makers, "are looking for impartial, technically reliable and timely assessments about its speed of progress and impact."