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

arXiv: https://arxiv.org/abs/2601.04135 · HTML: https://mlsanity.com/paper/2601.04135/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech](https://mlsanity.com/paper/2107.08720/) (2021)
- [SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours](https://mlsanity.com/paper/1704.05972/) (2017)
- [ConvoKit: A Toolkit for the Analysis of Conversations](https://mlsanity.com/paper/2005.04246/) (2020)
- [Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop](https://mlsanity.com/paper/1909.01362/) (2019)
- [Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering](https://mlsanity.com/paper/2211.03433/) (2022)
- [Countering Misinformation via Emotional Response Generation](https://mlsanity.com/paper/2311.10587/) (2023)
- [Do LLMs suffer from Multi-Party Hangover? A Diagnostic Approach to Addressee Recognition and Response Selection in Conversations](https://mlsanity.com/paper/2409.18602/) (2024)
- [Putting Context in Context: the Impact of Discussion Structure on Text Classification](https://mlsanity.com/paper/2402.02975/) (2024)
- [Fine-tuning with HED-IT: The impact of human post-editing for dialogical language models](https://mlsanity.com/paper/2406.07288/) (2024)
- [Don't Stop the Multi-Party! On Generating Synthetic Written Multi-Party Conversations with Constraints](https://mlsanity.com/paper/2502.13592/) (2025)

## Abstract (arXiv)

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.