Data-Driven Function Calling Improvements in Large Language Model for Online Financial QA
Large language models (LLMs) have been incorporated into numerous industrial applications. Meanwhile, a vast array of API assets is scattered across various functions in the financial domain. An online financial question-answering system can leverage both LLMs and private APIs to provide timely financial analysis and information. The key is equipping the LLM model with function calling capability tailored to a financial scenario. However, a generic LLM requires customized financial APIs to call and struggles to adapt to the financial domain. Additionally, online user queries are diverse and contain out-of-distribution parameters compared with the required function input parameters, which ...
| Authors | Xing Tang, Hao Chen, Shiwei Li et al. |
|---|---|
| Primary category | cs.IR |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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