FinGPT: Open-Source Financial Large Language Models
Not on the map. Why?
Cited by 32 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Large language models (LLMs) have shown the potential of revolutionizing natural language processing tasks in diverse domains, sparking great interest in finance. Accessing high-quality financial data is the first challenge for financial LLMs (FinLLMs). While proprietary models like BloombergGPT have taken advantage of their unique data accumulation, such privileged access calls for an open-source alternative to democratize Internet-scale financial data. In this paper, we present an open-source large language model, FinGPT, for the finance sector. Unlike proprietary models, FinGPT takes a data-centric approach, providing researchers and practitioners with accessible and transparent resources to develop their FinLLMs. We highlight the importance of an automatic data curation pipeline and the lightweight low-rank adaptation technique in building FinGPT. Furthermore, we showcase several potential applications as stepping stones for users, such as robo-advising, algorithmic trading, and low-code development. Through collaborative efforts within the open-source AI4Finance community, FinGPT aims to stimulate innovation, democratize FinLLMs, and unlock new opportunities in open finance. Two associated code repos are https://github.com/AI4Finance-Foundation/FinGPT and https://github.com/AI4Finance-Foundation/FinNLP
Key facts. Cited by 244 papers in the map (data through 7 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 9 Jun 2023.
Builds on
15 of 30 references.
References · 15
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
- 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
- 2023BloombergGPT: A Large Language Model for Financecited by 923
- 2019How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddingscited by 770
- 2021FinQA: A Dataset of Numerical Reasoning over Financial Datacited by 552
- 2019FinBERT: Financial Sentiment Analysis with Pre-trained Language Modelscited by 378
3 more
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Citing papers per year: 2023: 46; 2024: 78; 2025: 52; 2026: 68.
244 papers.
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