Can LLMs Answer Investment Banking Questions? Using Domain-Tuned Functions to Improve LLM Performance on Knowledge-Intensive Analytical Tasks


  • Nicholas Harvel ModuleQ, Inc.
  • Felipe Bivort Haiek ModuleQ, Inc.
  • Anupriya Ankolekar ModuleQ, Inc.
  • David James Brunner ModuleQ, Inc.



Question Answering, Domain Knowledge, Large Language Models, Prompt Engineering, LLM Function Calling, Chain Of Thought Reasoning, Zero-shot Prompting, Data Analysis, Information Retrieval, Investment Banking


Large Language Models (LLMs) can increase the productivity of general-purpose knowledge work, but accuracy is a concern, especially in professional settings requiring domain-specific knowledge and reasoning. To evaluate the suitability of LLMs for such work, we developed a benchmark of 16 analytical tasks representative of the investment banking industry. We evaluated LLM performance without special prompting, with relevant information provided in the prompt, and as part of a system giving the LLM access to domain-tuned functions for information retrieval and planning. Without access to functions, state-of-the-art LLMs performed poorly, completing two or fewer tasks correctly. Access to appropriate domain-tuned functions yielded dramatically better results, although performance was highly sensitive to the design of the functions and the structure of the information they returned. The most effective designs yielded correct answers on 12 out of 16 tasks. Our results suggest that domain-specific functions and information structures, by empowering LLMs with relevant domain knowledge and enabling them to reason in domain-appropriate ways, may be a powerful means of adapting LLMs for use in demanding professional settings.






Empowering Machine Learning and Large Language Models with Domain and Commonsense Knowledge