GUIDE 框架：让 LLM 自学通用多方会议主持策略
Learning to GUIDE the Group: Self-Learning LLM Facilitation Across Multi-Party Tasks
一组研究者让 LLM 自己总结出怎么主持多人讨论，胜过三个专用主持人，还更省话，做群聊或会议助手的可以看看。
论文提出 GUIDE 框架，用 432 段多人对话数据训练通用 LLM 主持人。通过自学习方法，LLM 从专家主持对话中归纳策略手册，再用与 20 名人类标注者对齐的 LLM judge 迭代优化。在留出对话上，该通用主持人对三个任务专用专家主持人达到 61.4% 胜率；在线实测中说话舒适度提升、人际摩擦下降，回合数减少 24.7%、字数减少 29.0%。
Learning to GUIDE the Group: Self-Learning LLM Facilitation Across Multi-Party Tasks
As large language models 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. 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 strategy offline to real-time interventions. We first collect multi-party human interactions guided by task-specific expert LLM facilitators (N=432 conversations). We then employ a self-learning method in which an LLM induces an actionable strategic playbook from conversations 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, 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.
