LLMberjack: Guided Trimming of Debate Trees for Multi-Party Conversation Creation

2026-01-07

arXiv.org

AI Summary

This work addresses the scarcity of high-quality, well-structured multiparty dialogue data and the challenge of efficiently generating coherent linear dialogues from raw debate trees. The authors propose an interactive pruning method that integrates debate tree visualization with large language model (LLM) assistance, enabling users to guide the construction of multi-turn dialogues while preserving speaker identities and discourse relationships. This approach establishes the first transparent and reproducible pipeline for multiparty dialogue generation, significantly enhancing dialogue coherence and semantic quality while reducing manual editing effort. The accompanying open-source platform fills a critical gap in available resources for this research domain.

Technology Category

Natural Language Processing: Discourse, Pragmatics & Argument MiningMachine Learning: Large Multimodal Models (LMMs)Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSearch and Retrieval-Augmented AI: Assisted, interactive, and conversational searchUser Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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.

Problem

• multi-party conversation
• debate trees
• conversation creation
• discourse relations
• participant identity

Innovation

• multi-party conversation
• debate tree trimming
• LLM-assisted editing
• interactive visualization
• dialogue linearization