  We propose Multiple Experts Fine-tuning Framework to build a financial large
language model (LLM), DISC-FinLLM. Our methodology improves general LLMs by
endowing them with multi-turn question answering abilities, domain text
processing capabilities, mathematical computation skills, and
retrieval-enhanced generation capabilities. We build a financial
instruction-tuning dataset named DISC-FIN-SFT, including instruction samples of
four categories (consulting, NLP tasks, computing and retrieval-augmented
generation). Evaluations conducted on multiple benchmarks demonstrate that our
model performs better than baseline models in various financial scenarios.
Further resources can be found at https://github.com/FudanDISC/DISC-FinLLM.
