# To Facilitate or not to Facilitate: Human and LLM Facilitator Tendencies in Online Discussions

arXiv: https://arxiv.org/abs/2607.28643 · HTML: https://mlsanity.com/paper/2607.28643/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 15 of 15)

- [Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.04388/) (2023)
- [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://mlsanity.com/paper/2412.13663/) (2024)
- [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021)
- [Opportunities and Risks of LLMs for Scalable Deliberation with Polis](https://mlsanity.com/paper/2306.11932/) (2023)
- [Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop](https://mlsanity.com/paper/1909.01362/) (2019)
- [Conversational flow in Oxford-style debates](https://mlsanity.com/paper/1604.03114/) (2016)
- [I Beg to Differ: A study of constructive disagreement in online conversations](https://mlsanity.com/paper/2101.10917/) (2021)
- [WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community](https://mlsanity.com/paper/1810.13181/) (2018)
- [Make Reddit Great Again: Assessing Community Effects of Moderation Interventions on r/The_Donald](https://mlsanity.com/paper/2201.06455/) (2022)
- [Evaluation and Facilitation of Online Discussions in the LLM Era: A Survey](https://mlsanity.com/paper/2503.01513/) (2025)
- [Venire: A Machine Learning-Guided Panel Review System for Community Content Moderation](https://mlsanity.com/paper/2410.23448/) (2024)
- [How to disagree well: Investigating the dispute tactics used on Wikipedia](https://mlsanity.com/paper/2212.08353/) (2022)
- [WHoW: A Cross-domain Approach for Analysing Conversation Moderation](https://mlsanity.com/paper/2410.15551/) (2024)
- [Moderation Matters:Measuring Conversational Moderation Impact in English as a Second Language Group Discussion](https://mlsanity.com/paper/2502.18341/) (2025)
- [Designing Synthetic Discussion Generation Systems: A Case Study for Online Facilitation](https://mlsanity.com/paper/2503.16505/) (2025)

## Built on by (most cited first, 1 of 1)

- [Human and LLM Facilitation in Collaborative Design Meetings: A Computational Behavioral Comparison](https://mlsanity.com/paper/2610.13082/) (2026)

## Abstract (arXiv)

Automating facilitation in online discussions is a long-standing social concern given the increasing time we spend on online spaces and the failure of content moderation approaches. While studies have been conducted on how to facilitate, none have answered the essential question of when to do so. A potential answer is using LLMs, which ostensibly make automated, large-scale intervention increasingly feasible. In this study, we examine when LLMs decide to facilitate by defining what facilitation is, observing when humans decide to facilitate, and comparing their decisions with those made by LLMs. To this end, we create PEFK, a corpus standardizing and aggregating all relevant facilitation datasets. We are the first to run a survey on facilitation timing, which we execute using expert facilitative participants and LLM-as-a-judge models. We discover that while humans are more cautious, LLMs are excessively eager to facilitate, although both are more certain when judging that facilitation is not needed. We then investigate whether this behavior can be corrected using alternative setups for LLMs and training ModernBert classifiers on established datasets, finding that the latter perform more reliably than the former, although current datasets impose a relatively low performance ceiling.