用对话树生成多人对话，支持智能编辑与可视化协作。
LLMberjack: Guided Trimming of Debate Trees for Multi-Party Conversation Creation
- 将辩论树转为连贯的多人对话序列，保持发言者身份与逻辑关系。
- 结合LLM辅助编辑，提升对话质量并减少人工工作量。
- 开源工具，适合需要透明可复现对话生成的研究者使用。
我们提出LLMberjack，一个从原有辩论树结构出发生成多人对话的平台。该系统提供交互式界面，可视化讨论树，并支持用户构建保持参与者身份与话语关系的连贯线性对话。平台集成可选的大语言模型（LLM）辅助功能，用于自动编辑消息内容及发言者描述。通过实证展示，树状可视化有助于生成连贯有意义的对话线程，而LLM支持能提升输出质量并降低人工投入。该工具开源，旨在推动多参与者对话生成的透明与可复现流程，弥补此类资源的不足。
原文摘要 · Abstract (English)
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.
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