Designing Synthetic Discussion Generation Systems: A Case Study for Online Facilitation

Abstract

A critical challenge in social science research is the high cost associated with experiments involving human participants. We identify Synthetic Discussion Generation (SDG), a novel Natural Language Processing (NLP) direction aimed at creating simulated discussions that enable cost-effective pilot experiments and develop a theoretical, task-agnostic framework for designing, evaluating, and implementing these simulations. We argue that the use of proprietary models such as the OpenAI GPT family for such experiments is often unjustified in terms of both cost and capability, despite its prevalence in current research. Our experiments demonstrate that smaller quantized models (7B-8B) can produce effective simulations at a cost more than 44 times lower compared to their proprietary counterparts. We use our framework in the context of online facilitation, where humans actively engage in discussions to improve them, unlike more conventional content moderation. By treating this problem as a downstream task for our framework, we show that synthetic simulations can yield generalizable results at least by revealing limitations before engaging human discussants. In LLM facilitators, a critical limitation is that they are unable to determine when to intervene in a discussion, leading to undesirable frequent interventions and, consequently, derailment patterns similar to those observed in human interactions. Additionally, we find that different facilitation strategies influence conversational dynamics to some extent. Beyond our theoretical SDG framework, we also present a cost-comparison methodology for experimental design, an exploration of available models and algorithms, an open-source Python framework, and a large, publicly available dataset of LLM-generated discussions across multiple models.