LLMberjack: 多方对话创建的引导修剪
LLMberjack: Guided Trimming of Debate Trees for Multi-Party Conversation Creation
AI总结：
LLMberjack通过引导修剪辩论树，利用LLM辅助实现多方对话的高效创建，提升输出质量并减少人工工作量。
摘要：
我们提出了LLMberjack，一个从现有辩论开始创建多方对话的平台，原有结构为回复树。该系统提供一个交互式界面，可视化讨论树，并允许用户构建连贯的线性对话序列，同时保持参与者身份和话语关系。它整合了可选的大语言模型（LLM）辅助，以支持自动编辑信息和说话者的描述。我们通过展示树可视化如何促进创建连贯、有意义的对话线，以及LLM支持如何提高输出质量并减少人力工作量来展示该平台的实用性。该工具是开源的，旨在促进透明和可重复的工作流程，以创建多方对话，解决此类资源匮乏的问题。
英文摘要：
We present LLMberjack, a platform for creating multi-party conversations starting from existing debates, originally structured as reply trees. The system offers an interactive interface that visualizes discussion trees and enables users to construct coherent linearized dialogue sequences while preserving participant identity and discourse relations. It integrates optional large language model (LLM) assistance to support automatic editing of the messages and speakers' descriptions. We demonstrate the platform's utility by showing how tree visualization facilitates the creation of coherent, meaningful conversation threads and how LLM support enhances output quality while reducing human effort. The tool is open-source and designed to promote transparent and reproducible workflows to create multi-party conversations, addressing a lack of resources of this type.
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。
