Conversational Financial Information Retrieval Model (ConFIRM)
Stephen Choi, William Gazeley, Siu Ho Wong, Tingting Li
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.