Debt collection negotiations (DCN) are vital for managing non-performing loans (NPLs) and reducing creditor losses. Traditional methods are labor-intensive, while large language models (LLMs) offer promising automation potential. However, prior systems lacked dynamic negotiation and real-time decision-making capabilities. This paper explores LLMs in automating DCN and proposes a novel evaluation framework with 13 metrics across 4 aspects. Our experiments reveal that LLMs tend to over-concede compared to human negotiators. To address this, we propose the Multi-Agent Debt Negotiation (MADeN) framework, incorporating planning and judging modules to improve decision rationality. We also apply post-training techniques, including DPO with rejection sampling, to optimize performance. Our studies provide valuable insights for practitioners and researchers seeking to enhance efficiency and outcomes in this domain.

翻译：债务催收谈判对于管理不良贷款和减少债权人损失至关重要。传统方法劳动密集，而大型语言模型展现出有前景的自动化潜力。然而，现有系统缺乏动态谈判和实时决策能力。本文探讨了大型语言模型在债务催收谈判自动化中的应用，并提出一个包含4个维度、13项指标的新型评估框架。实验表明，与人类谈判者相比，大型语言模型倾向于过度让步。为解决此问题，我们提出了多智能体债务谈判框架，通过集成规划与裁决模块提升决策合理性。同时，我们应用了包含拒绝采样的直接偏好优化等后训练技术以优化性能。本研究为寻求提升该领域效率与成果的从业者和研究者提供了重要见解。