Learning to GUIDE the Group: Self-Learning LLM Facilitation Across Multi-Party Tasks

As large language models (LLMs) are increasingly adopted as multi-party communication facilitators, there is a growing demand for agents capable of balancing diverse group dynamics across contexts. However, existing LLM facilitators are often fine-tuned for task-specific objectives; generalized LLM-based facilitation methods that can adapt to different types of tasks and diverse group dynamics are underdeveloped. To address this gap, we present GUIDE (Generalized Unified Intervention for Dynamic Exchanges), a framework for developing generalized LLM-based facilitators across different multi-party contexts, such as open-ended discussion, negotiation, and consensus-building. GUIDE evaluates facilitation across both objective task outcomes and subjective metrics, including psychological safety and perceived friction. Within this framework, we study whether LLMs can self-learn generalized facilitation prompt updates offline to real-time interventions. We first collect a corpus of multi-party human interactions guided by task-specific expert LLM facilitators (N=432 conversations). Grounded in these dialogues, we then employ a self-learning method in which an LLM induces an actionable strategic playbook and iteratively refines it using feedback from an LLM judge aligned with human annotators (N=20), developing a single generalized facilitator that outperforms the three task-specific expert facilitators on held-out conversations (61.4% win rate). Finally, we evaluate these improvements empirically; in a real-time online empirical study (N=462 conversations), the self-learned generalized facilitator preserves facilitator performance and group productivity, improves speaking comfort and reduces interpersonal friction, while using 24.7% fewer turns and 29.0% fewer words than the expert prompts. These findings provide empirical evidence that LLMs can learn generalizable, task-agnostic social facilitation from task-specific human interactions, on-par with hand-crafted task-specific prompts across diverse group settings.