大规模语言模型在债务催收谈判中的应用：一个评估系统与基于多智能体的决策优化
Debt Collection Negotiations with Large Language Models: An Evaluation System and Optimizing Decision Making with Multi-Agent
AI总结：
本文提出包含13项指标的评估框架，发现LLMs在债务催收谈判中过度让步，并引入MADeN多智能体框架及后训练技术以优化决策，提升谈判效率与成果。
摘要：
债务催收谈判（DCN）对于管理不良贷款（NPLs）和减少债权人损失至关重要。传统方法劳动密集，而大规模语言模型（LLMs）提供了有前景的自动化潜力。然而，先前的系统缺乏动态谈判和实时决策能力。本文探讨了LLMs在自动化DCN中的应用，并提出了一个包含4个方面13项指标的新颖评估框架。我们的实验表明，与人类谈判者相比，LLMs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（MADeN）框架，整合了规划和评判模块以提高决策理性。我们还应用了后训练技术，包括带拒绝采样的DPO，以优化性能。我们的研究为寻求提升该领域效率和成果的从业者和研究人员提供了宝贵的见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
