信用C-GPT：一种面向越南债务催收的领域专用大语言模型
Credit C-GPT: A Domain-Specialized Large Language Model for Conversational Understanding in Vietnamese Debt Collection
AI总结：
Credit C-GPT是一种针对越南债务催收场景优化的领域专用大语言模型，通过整合多任务对话智能，提升对话理解、情感识别和意图检测等能力，为企业呼叫中心提供实时帮助和分析解决方案。
摘要：
债务催收是银行、金融服务和保险（BFSI）行业中的关键职能，依赖于大量在越南呼叫中心进行的人与人之间的大规模对话互动。这些对话涉及非正式的口语语言、情感变化和复杂的领域特定推理，这对传统自然语言处理系统构成了重大挑战。本文介绍了Credit C-GPT，一种具有70亿参数的领域专用大语言模型，经过微调以适应越南债务催收场景中的对话理解。所提出的模型整合了多个对话智能任务，包括对话理解、情感识别、意图检测、通话阶段分类和结构化槽值提取，均在单一推理框架内完成。我们描述了数据构建过程、标注策略和训练方法，并在专有的人工标注数据集上评估了该模型。实验结果表明，与传统流水线方法相比，该模型在多个指标上均表现出一致的提升，表明领域专用的对话语言模型为企业呼叫中心的实时帮助和通话后分析提供了可扩展且隐私友好的解决方案。
英文摘要：
Debt collection is a critical function within the banking, financial services, and insurance (BFSI) sector, relying heavily on large-scale human-to-human conversational interactions conducted primarily in Vietnamese contact centers. These conversations involve informal spoken language, emotional variability, and complex domain-specific reasoning, which pose significant challenges for traditional natural language processing systems. This paper introduces Credit C-GPT, a domain-specialized large language model with seven billion parameters, fine-tuned for conversational understanding in Vietnamese debt collection scenarios. The proposed model integrates multiple conversational intelligence tasks, including dialogue understanding, sentiment recognition, intent detection, call stage classification, and structured slot-value extraction, within a single reasoning-based framework. We describe the data construction process, annotation strategy, and training methodology, and evaluate the model on proprietary human-annotated datasets. Experimental results show consistent improvements over traditional pipeline-based approaches, indicating that domain-specialized conversational language models provide a scalable and privacy-aware solution for real-time assistance and post-call analytics in enterprise contact centers.
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。
