aiqbench analysis
The paper presents an open-source interactive tool for turning Reddit- or Kialo-style reply trees into multi-speaker dialogues, with optional LLM help for editing messages and speaker descriptions. Its main value is a reproducible resource for building multi-party conversation datasets, which is a gap in NLP work. Readers should note that the evidence is described as preliminary, and the LLM gains in quality and effort are reported without detailed benchmarks in the abstract.
LLMberjack: Guided Trimming of Debate Trees for Multi-Party Conversation Creation
arXiv:2601.04135v2 Announce Type: replace Abstract: Online debates on platforms like Reddit or Kialo are structured as reply trees: each message can receive several replies, which in turn can each receive several replies, producing a branching structure rather than a linear conversation. Turning one path (or a mix of paths) through such a tree into a natural, turn-by-turn conversation among multiple speakers (a process we call linearization) requires deciding which messages to keep, in what order, and who each message is addressed to. To this aim, 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 provide preliminary evidence about 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.
Abstract:Online debates on platforms like Reddit or Kialo are structured as reply trees: each message can receive several replies, which in turn can each receive several replies, producing a branching structure rather than a linear conversation. Turning one path (or a mix of paths) through such a tree into a natural, turn-by-turn conversation among multiple speakers (a process we call linearization) requires deciding which messages to keep, in what order, and who each message is addressed to. To this aim, 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 provide preliminary evidence about 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.
Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
arXiv:2601.04135 [cs.CL]
(or arXiv:2601.04135v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2601.04135 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 294 words