Debate-to-Write: A Persona-Driven Multi-Agent Framework for Diverse Argument Generation
This addresses the challenge of limited output diversity and coherence in argument generation for AI systems, though it appears incremental as it builds on existing multi-agent and persona-based approaches.
The authors tackled the problem of generating diverse and persuasive arguments by proposing a persona-driven multi-agent framework that simulates debate, resulting in improved diversity and persuasiveness in argumentative essays as shown in evaluations.
Writing persuasive arguments is a challenging task for both humans and machines. It entails incorporating high-level beliefs from various perspectives on the topic, along with deliberate reasoning and planning to construct a coherent narrative. Current language models often generate surface tokens autoregressively, lacking explicit integration of these underlying controls, resulting in limited output diversity and coherence. In this work, we propose a persona-based multi-agent framework for argument writing. Inspired by the human debate, we first assign each agent a persona representing its high-level beliefs from a unique perspective, and then design an agent interaction process so that the agents can collaboratively debate and discuss the idea to form an overall plan for argument writing. Such debate process enables fluid and nonlinear development of ideas. We evaluate our framework on argumentative essay writing. The results show that our framework can generate more diverse and persuasive arguments through both automatic and human evaluations.