With the exponential growth in large language models (LLMs), leveraging their emergent properties for specialized domains like finance merits exploration. However, regulated fields such as finance pose unique constraints, requiring domain-optimized frameworks. We present ConFIRM, an LLM-based conversational financial information retrieval model tailored for query intent classification and knowledge base labeling. ConFIRM comprises two modules: 1) a method to synthesize finance domain-specific question-answer pairs, and 2) evaluation of parameter efficient fine-tuning approaches for the query classification task. We generate a dataset of over 4000 samples, assessing accuracy on a separate test set. ConFIRM achieved over 90% accuracy, essential for regulatory compliance. ConFIRM provides a data-efficient solution to extract precise query intent for financial dialog systems.

翻译：随着大语言模型（LLMs）的指数级增长，利用其涌现特性服务于金融等专业领域值得深入探索。然而，金融等受监管领域存在独特约束，需要领域优化的框架。我们提出ConFIRM——一种基于大语言模型的对话式金融信息检索模型，专门用于查询意图分类和知识库标注。ConFIRM包含两个模块：1) 合成金融领域特定问答对的方法；2) 评估用于查询分类任务的参数高效微调方案。我们生成了超过4000个样本的数据集，并在独立测试集上评估准确率。ConFIRM实现了超过90%的准确率，这对满足监管合规要求至关重要。ConFIRM为金融对话系统提供了一种数据高效的解决方案，用于提取精确的查询意图。