# CFGPT: Chinese Financial Assistant with Large Language Model arXiv: https://arxiv.org/abs/2309.10654 · HTML: https://mlsanity.com/paper/2309.10654/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 40 references that are in the map. - First posted on arXiv 19 Sep 2023. ## Builds on (most cited first, 27 of 27) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023) - [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022) - [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021) - [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023) - [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://mlsanity.com/paper/2204.06745/) (2022) - [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://mlsanity.com/paper/2109.00122/) (2021) - [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019) - [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013) - [FinGPT: Open-Source Financial Large Language Models](https://mlsanity.com/paper/2306.06031/) (2023) - [PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance](https://mlsanity.com/paper/2306.05443/) (2023) - [ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering](https://mlsanity.com/paper/2210.03849/) (2022) - [FinBERT: A Pretrained Language Model for Financial Communications](https://mlsanity.com/paper/2006.08097/) (2020) - [PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation](https://mlsanity.com/paper/2104.12369/) (2021) - [WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain](https://mlsanity.com/paper/2211.00083/) (2022) - [XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters](https://mlsanity.com/paper/2305.12002/) (2023) - [Impact of News on the Commodity Market: Dataset and Results](https://mlsanity.com/paper/2009.04202/) (2020) - [BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark](https://mlsanity.com/paper/2302.09432/) (2023) - [FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance](https://mlsanity.com/paper/2111.09395/) (2021) - [FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2308.09975/) (2023) - [Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https://mlsanity.com/paper/2110.06696/) (2021) - [Self-QA: Unsupervised Knowledge Guided Language Model Alignment](https://mlsanity.com/paper/2305.11952/) (2023) ## Built on by (most cited first, 11 of 11) - [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024) - [CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models](https://mlsanity.com/paper/2407.02301/) (2024) - [RealFin: How Well Do LLMs Reason About Finance When Users Leave Things Unsaid?](https://mlsanity.com/paper/2602.07096/) (2026) - [From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery](https://mlsanity.com/paper/2605.15412/) (2026) - [Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models](https://mlsanity.com/paper/2411.06272/) (2024) - [Bridging Language Models and Financial Analysis](https://mlsanity.com/paper/2503.22693/) (2025) - [Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges](https://mlsanity.com/paper/2507.18577/) (2025) - [Financial Knowledge Large Language Model](https://mlsanity.com/paper/2407.00365/) (2024) - [Scalable Multi-Domain Adaptation of Language Models using Modular Experts](https://mlsanity.com/paper/2410.10181/) (2024) - [FinLMM-R1: Enhancing Financial Reasoning in LMM through Scalable Data and Reward Design](https://mlsanity.com/paper/2506.13066/) (2025) - [FinBloom: Knowledge Grounding Large Language Model with Real-time Financial Data](https://mlsanity.com/paper/2502.18471/) (2025) ## Abstract (arXiv) Large language models (LLMs) have demonstrated great potential in natural language processing tasks within the financial domain. In this work, we present a Chinese Financial Generative Pre-trained Transformer framework, named CFGPT, which includes a dataset~(CFData) for pre-training and supervised fine-tuning, a financial LLM~(CFLLM) to adeptly manage financial texts, and a deployment framework~(CFAPP) designed to navigate real-world financial applications. The CFData comprising both a pre-training dataset and a supervised fine-tuning dataset, where the pre-training dataset collates Chinese financial data and analytics, alongside a smaller subset of general-purpose text with 584M documents and 141B tokens in total, and the supervised fine-tuning dataset is tailored for six distinct financial tasks, embodying various facets of financial analysis and decision-making with 1.5M instruction pairs and 1.5B tokens in total. The CFLLM, which is based on InternLM-7B to balance the model capability and size, is trained on CFData in two stage, continued pre-training and supervised fine-tuning. The CFAPP is centered on large language models (LLMs) and augmented with additional modules to ensure multifaceted functionality in real-world application. Our codes are released at https://github.com/TongjiFinLab/CFGPT.