DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning
This work addresses the need for specialized AI in finance, though it appears incremental as it builds on existing fine-tuning methods for a specific domain.
The authors tackled the problem of adapting general large language models to financial domains by proposing DISC-FinLLM, a model fine-tuned with a multiple experts framework, which outperformed baseline models on various financial benchmarks.
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.