Domain-Specific Large Language Model (LLM)
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A Domain-Specific Large Language Model (LLM) is an LLM that is a domain-specific model.
- Context:
- It can be designed to perform a wide range of NLP tasks, including sentiment analysis, named entity recognition, question answering, and document summarization, with a high degree of accuracy and relevance within its domain.
- It can leverage extensive pre-training on large, domain-specific corpora, often supplemented with general language data, to ensure a comprehensive understanding of both domain-specific nuances and common language.
- It can significantly enhance the efficiency and accuracy of automated systems in domain-specific applications, offering insights, analyses, or support that would otherwise require expert human intervention.
- It can be open-source or proprietary, depending on the model's developers and intended use, with some models like FinGPT being made available to the public to foster innovation and research within the financial sector.
- ...
- Example(s):
- a Financial-Domain LLM, such as:
- BloombergGPT, developed to support various financial NLP tasks.
- FinGPT, an open-source financial LLM designed to provide factual information through analysis of vast amounts of financial data.
- ...
- a Legal-Domain LLM.
- a Financial-Domain LLM, such as:
- Counter-Example(s):
- See: BloombergGPT, FinGPT, Healthcare LLM, Legal Domain LLM.
- See: BloombergGPT, FinGPT.
References
2023
- GBard
- FinGPT is an open-source financial large language model (LLM) developed by the AI4Finance Foundation. It is a collection of seven monolingual models trained from scratch (186M to 13B parameters) and a 176 billion-parameter multilingual model called BLUUMI. FinGPT is designed to provide factual information based on rigorous analysis of vast amounts of data.
2023
- (Wu, Irsoy et al., 2023) ⇒ Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann. (2023). “BloombergGPT: A Large Language Model for Finance.” In: arXiv preprint arXiv:2303.17564. doi:10.48550/arXiv.2303.17564
- ABSTRACT: The use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering. Large Language Models (LLMs) have been shown to be effective on a variety of tasks; however, no LLM specialized for the financial domain has been reported in literature. In this work, we present BloombergGPT, a 50 billion parameter language model that is trained on a wide range of financial data. We construct a 363 billion token dataset based on Bloomberg's extensive data sources, perhaps the largest domain-specific dataset yet, augmented with 345 billion tokens from general purpose datasets.