用AI聊天机器人帮合规人员快速准确回答监管问题。
Advancing Risk and Quality Assurance: A RAG Chatbot for Improved Regulatory Compliance
- 结合检索增强生成与混合搜索，提升问答准确性。
- 在124个真实查询上表现优于传统RAG方法。
- 提供可落地的参数配置建议，适合企业合规团队使用。
高度监管行业中的风险与质量（R&Q）保障需要持续应对复杂的监管框架，员工每天需处理大量需精准解读政策的咨询。传统依赖专业专家的方法导致操作瓶颈且难以扩展。本文提出一种新型检索增强生成（RAG）系统，利用大语言模型（LLMs），结合混合搜索与相关性增强机制，优化R&Q查询处理。在124个专家标注的真实查询上评估，该系统在实际部署中显著优于传统RAG方法。此外，我们进行了全面的超参数分析，比较多种配置方案，为实践者提供宝贵参考。
原文摘要 · Abstract (English)
Risk and Quality (R&Q) assurance in highly regulated industries requires constant navigation of complex regulatory frameworks, with employees handling numerous daily queries demanding accurate policy interpretation. Traditional methods relying on specialized experts create operational bottlenecks and limit scalability. We present a novel Retrieval Augmented Generation (RAG) system leveraging Large Language Models (LLMs), hybrid search and relevance boosting to enhance R&Q query processing. Evaluated on 124 expert-annotated real-world queries, our actively deployed system demonstrates substantial improvements over traditional RAG approaches. Additionally, we perform an extensive hyperparameter analysis to compare and evaluate multiple configuration setups, delivering valuable insights to practitioners.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。