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

Source: arXiv - 2601.04135v1

Overview

The paper introduces LLMberjack, an open‑source platform that turns complex debate trees—where each reply branches into new sub‑replies—into clean, linear multi‑party conversations. By visualizing the original tree and optionally leveraging large language models (LLMs) for smart editing, the tool lets researchers and developers quickly generate realistic dialogue data while preserving speaker identities and discourse relations.

Key Contributions

• Interactive Tree‑to‑Dialogue Interface – A visual UI that lets users navigate, prune, and linearize debate reply trees into coherent conversation scripts.
• LLM‑Assisted Editing – Optional integration of LLMs to automatically rewrite messages, smooth transitions, and generate concise speaker descriptions, cutting manual effort.
• Preservation of Discourse Structure – The platform maintains speaker turns, stance, and relational cues (e.g., rebuttal, support) during linearization.
• Open‑Source, Reproducible Workflow – All code, data pipelines, and documentation are released publicly, encouraging community extensions and benchmarking.
• Empirical Evaluation – User studies demonstrate that LLM assistance improves output quality (readability, coherence) while reducing the time needed to craft multi‑party dialogues.

Methodology

1. Data Ingestion – Existing debate datasets (e.g., Reddit r/ChangeMyView, online forums) are parsed into a reply tree where each node contains a message and its author.
2. Tree Visualization – The UI renders the tree with expandable/collapsible branches, allowing users to explore conversation flow and select sub‑trees of interest.
3. Guided Trimming – Users iteratively prune irrelevant branches and reorder nodes to produce a linear sequence that still respects the original discourse relations.
4. LLM Integration (optional) – When enabled, a downstream LLM receives the selected messages and speaker metadata, then:
   • Rewrites overly verbose or noisy posts into concise utterances.
   • Generates short, consistent speaker bios.
   • Inserts connective phrases to improve flow.
5. Export – The final dialogue can be exported in common formats (JSON, CSV, plain text) for downstream tasks such as dialogue modeling, chat‑bot training, or sociolinguistic analysis.

The workflow is deliberately modular: developers can swap the LLM backend (e.g., GPT‑4, LLaMA, open‑source alternatives) or plug in custom post‑processing scripts.

Results & Findings

• Quality Boost – In a blind evaluation with 30 participants, dialogues edited with LLM assistance scored 23 % higher on coherence and 18 % higher on naturalness compared to manual‑only outputs.
• Efficiency Gains – Average time to produce a 10‑turn conversation dropped from 12 min (manual) to 5 min (LLM‑assisted).
• Preservation of Stance – Automated trimming retained > 95 % of original speaker stance labels, confirming that the linearization does not erase argumentative intent.
• Scalability – The platform successfully processed debate trees with up to 1,200 nodes, demonstrating that even large, messy discussions can be distilled into manageable dialogues.

Practical Implications

• Data Generation for Conversational AI – Developers can quickly build high‑quality multi‑party dialogue corpora for training chat‑bots, virtual assistants, or debate‑style agents without hand‑crafting every conversation.
• Synthetic Test Sets – Researchers can generate controlled conversation scenarios (e.g., multi‑speaker conflict, collaborative problem solving) to benchmark dialogue systems on nuanced interaction patterns.
• Content Moderation & Analysis – By preserving discourse relations, the tool aids in creating labeled datasets for stance detection, argument mining, and toxicity analysis across multiple participants.
• Educational & Training Simulations – Educators can turn real debate archives into role‑play scripts for classroom debates, negotiation training, or persuasive communication workshops.
• Rapid Prototyping – The open‑source nature lets product teams integrate LLMberjack into internal pipelines, automating the conversion of community forums or support tickets into structured dialogue logs for analytics.

Limitations & Future Work

• LLM Dependency – The quality boost hinges on the underlying LLM; cheaper or less capable models may produce sub‑par rewrites, requiring careful model selection.
• Bias Propagation – Since the source debates inherit community biases, the generated dialogues can reflect those biases unless additional filtering is applied.
• Limited Language Support – Current implementation focuses on English datasets; extending to multilingual debates will need language‑specific tokenizers and LLMs.
• User Interaction Overhead – While the UI streamlines trimming, complex trees still demand manual decisions; future work aims to add semi‑automated branch‑selection heuristics.
• Evaluation Scope – The user study involved a modest number of participants and domains; broader evaluations across varied debate platforms (political forums, scientific discussions) are planned.

By addressing these gaps, the authors envision LLMberjack becoming a staple tool for anyone needing realistic, multi‑speaker conversational data—bridging the gap between raw debate archives and the clean dialogue corpora that power today’s conversational AI.

Authors

• Leonardo Bottona
• Nicolò Penzo
• Bruno Lepri
• Marco Guerini
• Sara Tonelli

Paper Information

• arXiv ID: 2601.04135v1
• Categories: cs.CL, cs.HC
• Published: January 7, 2026
• PDF: Download PDF