![]() ![]() A finance-specific model will be able to improve existing financial NLP tasks, such as sentiment analysis, named entity recognition, news classification, and question answering, among others. It's not unlike other specialized domains, like medicine, which contain vocabulary you don't see in general-purpose text. While recent advances in AI models have demonstrated exciting new applications for many domains, the complexity and unique terminology of the financial domain warrant a domain-specific model. Why does finance need its own language model? Surprisingly, the model still performed on par on general-purpose benchmarks, even though we had aimed to build a domain-specific model. We found that BloombergGPT outperforms-by large margins!-existing models of a similar size on financial tasks. We trained a new model on this combined dataset and tested it across a range of language tasks on finance documents. The resulting dataset was about 700 billion tokens, which is about 30 times the size of all the text in Wikipedia. We took a novel approach and built a massive dataset of financial-related text and combined it with an equally large dataset of general-purpose text. In collaboration with Bloomberg, we explored this question by building an English language model for the financial domain. ![]() It's not clear what the best strategy is for building these models. While ChatGPT is impressive for many uses, we need specialized models for medicine, science, and many other domains. However, we also need domain-specific models that understand the complexities and nuances of a particular domain. To date, most models are focused on general-purpose use cases. The potential for these models to transform society is clear. Many people have seen ChatGPT and other large language models, which are impressive new artificial intelligence technologies with tremendous capabilities for processing language and responding to people's requests. What were the goals of the BloombergGPT project?
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