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DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning

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arxiv 2310.15205 v2 pith:C7UEFQ6R submitted 2023-10-23 cs.CL

Wei Chen , Qiushi Wang , Zefei Long , Xianyin Zhang , Zhongtian Lu , Bingxuan Li , Siyuan Wang , Jiarong Xu
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Xiang Bai Xuanjing Huang Zhongyu Wei
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classification cs.CL
keywords financialdisc-finllmmodelmultiplebuildcapabilitiesexpertsfine-tuning
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    Using LLM extraction on 681 papers, the authors build a public knowledge graph showing financial NLP moved from LLM adoption to limitation-aware, modular system design between 2022 and 2025.

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    Agentar-Fin-R1, an 8B and 32B financial LLM family, reports top scores on FinEval, FinanceIQ, and a new Finova benchmark while keeping general reasoning near its Qwen3 base.

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    cs.CE 2025-07 conditional novelty 5.0 of 10

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    A two-stage RL framework with length, image-selection, and adversarial rewards, trained on 89,378 ASP-built financial image-question pairs, improves multimodal reasoning over LMM-R1.

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