MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation

arXiv:2510.05124 · cs.CL, cs.AI, cs.CY, cs.HC, cs.MA · Submitted 2025-09-30 · Read on arXiv

cs.CL, cs.AI, cs.CY, cs.HC, cs.MA

Submitted: 2025-09-30

Updated: 2026-09-09

Comments: Accepted to EMNLP 2025 Industry Track (https://aclanthology.org/2025.emnlp-industry.26.pdf)

Code: https://github.com/openai/evals

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 5× in this paper · explained in 2 episodes
• Multi-Agent Dialogue Simulation — 5× in this paper
• Accuracy — 4× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Personalization — 3× in this paper · explained in 2 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Core — 2× in this paper · explained in 2 episodes
• Multi-Agent System — 2× in this paper · explained in 5 episodes
• Prompt Engineering — 2× in this paper · explained in 4 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Make Me Pay — 1× in this paper

Sources

• Evaluating Task-oriented Dialogue Systems: A Systematic Review of Measures, Constructs and their Operationalisations
• Simulation of Language Evolution under Regulated Social Media Platforms: A Synergistic Approach of Large Language Models and Genetic Algorithms
• On the Worst Prompt Performance of Large Language Models
• Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments
• Exploring Personality-Aware Interactions in Salesperson Dialogue Agents
• COOPER: Coordinating Specialized Agents towards a Complex Dialogue Goal
• Cohesive Conversations: Enhancing Authenticity in Multi-Agent Simulated Dialogues
• MMRole: A Comprehensive Framework for Developing and Evaluating Multimodal Role-Playing Agents
• LLMs Get Lost In Multi-Turn Conversation
• IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI Systems
• LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models
• Self-Refine: Iterative Refinement with Self-Feedback
• LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals
• Self-Refinement of Language Models from External Proxy Metrics Feedback
• Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
• Contrastive Speaker-Aware Learning for Multi-party Dialogue Generation with LLMs
• Muse: A Multimodal Conversational Recommendation Dataset with Scenario-Grounded User Profiles
• PersuasiveToM: A Benchmark for Evaluating Machine Theory of Mind in Persuasive Dialogues
• SpeechAgents: Human-Communication Simulation with Multi-Modal Multi-Agent Systems
• Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models

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