MALLM：多智能体大语言模型框架
MALLM: Multi-Agent Large Language Models Framework
AI总结：
MALLM是一个开源框架，通过提供多种配置选项，系统分析多智能体辩论的组件，促进对智能体角色、响应生成器、讨论范式和决策协议的深入理解。
摘要：
多智能体辩论（MAD）通过扩展测试时计算能力和利用专业知识，展示了增强集体智能的能力。当前的多智能体辩论框架往往偏向工具使用，缺乏集成评估，或提供有限的智能体角色、响应生成器、讨论范式和决策协议的可配置性。我们介绍了MALLM（多智能体大语言模型），一个开源框架，能够系统分析MAD组件。MALLM提供了超过144种MAD配置，包括（1）智能体角色（例如专家、性格）、（2）响应生成器（例如批判性、推理）、（3）讨论范式（例如记忆、接力）、（4）决策协议（例如投票、共识）。MALLM使用简单的配置文件定义辩论。此外，MALLM可以加载任何文本Hugging Face数据集（例如MMLU-Pro、WinoGrande），并提供评估流程，便于比较MAD配置。MALLM使研究人员能够系统地配置、运行和评估辩论，以解决他们的问题，促进对组件及其相互作用的理解。
英文摘要：
Multi-agent debate (MAD) has demonstrated the ability to augment collective intelligence by scaling test-time compute and leveraging expertise. Current frameworks for multi-agent debate are often designed towards tool use, lack integrated evaluation, or provide limited configurability of agent personas, response generators, discussion paradigms, and decision protocols. We introduce MALLM (Multi-Agent Large Language Models), an open-source framework that enables systematic analysis of MAD components. MALLM offers more than 144 unique configurations of MAD, including (1) agent personas (e.g., Expert, Personality), (2) response generators (e.g., Critical, Reasoning), (3) discussion paradigms (e.g., Memory, Relay), and (4) decision protocols (e.g., Voting, Consensus). MALLM uses simple configuration files to define a debate. Furthermore, MALLM can load any textual Hugging Face dataset (e.g., MMLU-Pro, WinoGrande) and provides an evaluation pipeline for easy comparison of MAD configurations. MALLM enables researchers to systematically configure, run, and evaluate debates for their problems, facilitating the understanding of the components and their interplay.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
