Large language models have increased due to the continuous development and advancement of artificial intelligence, which has profoundly impacted the state of natural language processing in various fields. The potential use of these models in the financial sector has attracted intense attention in light of this radical upheaval. However, building an effective and efficient open source economic language model depends on the collection of current, relevant, and high-quality data. The use of language models in the financial sector exposes many barriers. These range from challenges in obtaining data, maintaining various forms and types of data, and dealing with inconsistent data quality to the critical need for current information.
Extracting historical or specialized financial data becomes challenging due to various data sources, including web platforms, APIs, PDF documents, and photos. To train language models specifically for the banking industry, proprietary models like BloombergGPT have used their unique access to specialized data. However, the need for a more open and inclusive alternative has increased due to the limited accessibility and openness of its training and data collection processes. In response to this need, they see a changing trend toward the democratization of financial data at the Internet scale in the open source sector. Researchers from Columbia University and New York University (Shanghai) discuss similar problems with financial data in this research and provide FinGPT, an open source end-to-end framework for large language economic models (FinLLM).
FinGPT emphasizes the critical importance of data collection, cleansing, and pre-processing in creating open source FinLLMs using a data-centric approach. FinGPT seeks to advance financial research, cooperation, and innovation by promoting data accessibility and laying the groundwork for open financial practices. The following is a summary of their contributions: • Democratization: The open source FinGPT framework aims to democratize access to financial data and FinLLMs by exposing the unrealized promise of available funding. • Data-Centric Approach: Realizing the value of data curation, FinGPT takes a data-centric approach and employs strict cleansing and pre-processing techniques to handle various formats and types of data, resulting in data from high quality.
FinGPT adopts a complete framework for FinLLM with four layers which is an end-to-end framework.
– Data source layer: By capturing real-time information, this layer ensures complete market coverage while addressing the time sensitivity of financial data.
– The data engineering layer addresses the inherent difficulties of high time sensitivity and poor signal-to-noise ratio in financial data. It is ready for real-time NLP data processing.
– LLM Layer: Focusing on a variety of fine-tuning approaches, this layer reduces the highly dynamic nature of financial data and ensures the correctness and relevance of the model.
– Application Layer: This layer emphasizes the potential of FinGPT in the financial industry by showing real world applications and demos.
They want FinGPT to act as a catalyst to encourage innovation in the financial industry. In addition to its technical contributions, FinGPT fosters an open source environment for FinLLM, encouraging real-time processing and user-specific customization. FinGPT is positioned to change your understanding and use of FinLLM by fostering a strong ecosystem of cooperation within the open source AI4Finance community. Soon they plan to release the trained model.
review the Paper and GitHub link. Don’t forget to join our 23k+ ML SubReddit, discord channel, and electronic newsletter, where we share the latest AI research news, exciting AI projects, and more. If you have any questions about the article above or if we missed anything, feel free to email us at [email protected]
🚀 Check out 100 AI tools at AI Tools Club
Aneesh Tickoo is a consulting intern at MarktechPost. She is currently pursuing her bachelor’s degree in Information Science and Artificial Intelligence at the Indian Institute of Technology (IIT), Bhilai. She spends most of her time working on projects aimed at harnessing the power of machine learning. Her research interest is image processing and she is passionate about creating solutions around her. She loves connecting with people and collaborating on interesting projects.
