首个考虑用户行为差异的债务协商对话系统评测基准与优化模型
Everyone is unique: Towards Behaviorally Heterogeneous Negotiation Dialogue Systems for Debt Collection
- 构建包含多样化人格特征的债务协商数据集，模拟真实用户行为差异
- 提出DebtGPT模型，在回收率和用户体验上均优于开源基线，接近GPT-4o水平
- 为金融场景下的人机协商系统提供可复现的评估标准与训练框架
债务催收是金融行业关键的协商任务，兼具实际价值与学术意义，是研究以人为本对话系统的高风险、行为丰富测试平台。尽管大语言模型在对话与协商中展现出潜力，但其在复杂场景下的性能评估仍面临挑战：现有基准普遍假设用户为静态、理性的固定偏好主体，未能捕捉真实债务催收中的行为异质性。为此，我们提出DebtBench——首个公开的、基于人格特征的债务催收对话基准，突出行为多样性。同时，我们开发了DebtGPT，一个联合优化财务回收与交互体验的债务催收代理。使用16个主流LLM的实验结果表明，多数现有模型在此复杂但真实的场景中表现不佳，而DebtGPT优于所有开源基线，性能接近GPT-4o。代码与数据已公开于https://github.com/YYuHhhh/DebtNegotiation。
原文摘要 · Abstract (English)
Debt collection is a critical negotiation task in the financial industry, with strong practical relevance and exceptional academic value as a behaviorally rich, high-stakes testbed for human-centered dialogue systems. While large language models (LLMs) have shown promise in dialogue and negotiation, effectively evaluating their performance in this complex scenarios remains a major challenge: existing benchmarks uniformly assume users to be static, rational agents with fixed preferences, failing to capture the rich behavioral heterogeneity inherent in real-world debt collection. To bridge this gap, we propose DebtBench, the first public persona-enriched debt collection benchmark, that highlights behavioral heterogeneity in negotiation. Moreover, we develop DebtGPT, a debt collection agent trained to jointly optimize financial recovery and interaction experience. Our experimental results, using 16 state-of-the-art LLMs, find that most existing models struggle in this complex but realistic scenarios, whereas DebtGPT outperforms all open-source baselines and achieves performance on par with GPT-4o. The code and data are available at https://github.com/YYuHhhh/DebtNegotiation.
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