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  Bloomberg has released a research paper announcing the development of BloombergGPT, a new large-scale generative artificial intelligence model specifically trained on a wide range of financial data to support a diverse set of natural language processing (NLP) tasks within the financial industry. BloombergGPT will help improve existing financial NLP tasks, such as sentiment analysis, named entity recognition, news classification, and question answering, among others. It will also unlock new opportunities for marshalling the vast quantities of data available on the Bloomberg Terminal to better help the firm’s customers, while bringing the full potential of AI to the financial domain. To achieve this milestone, Bloomberg collaborated with its ML Product and Research group and AI Engineering team to construct one of the largest domain-specific datasets yet, drawing on the company’s existing data creation, collection, and curation resources. Using this data, the team trained a 50-billion parameter decoder-only causal language model that outperforms existing open models of a similar size on financial tasks by large margins, while still performing on par or better on general NLP benchmarks. The resulting model, BloombergGPT, will enable the company to tackle many new types of applications while delivering much higher performance out-of-the-box than custom models for each application, at a faster time-to-market. Thanks to the collection of financial documents Bloomberg has curated over four decades, the company was able to carefully create a large and clean, domain-specific dataset to train a LLM that is best suited for financial use cases. Shawn Edwards, Bloomberg’s Chief Technology Officer, said, “For all the reasons generative LLMs are attractive – few-shot learning, text generation, conversational systems, etc. – we see tremendous value in having developed the first LLM focused on the financial domain.” Gideon Mann, Head of Bloomberg’s ML Product and Research team, added, “We’re excited to use BloombergGPT to improve existing NLP workflows, while also imagining new ways to put this model to work to delight our customers.”