Domain-Specific Large Language Model (LLM)

A Domain-Specific Large Language Model (LLM) is an LLM model that is a domain-specific model.

• Context:
  • It can be designed to perform a wide range of Domain-Specific NLP tasks, including domain-specific sentiment analysis, domain-specific text generation, domain-specific question answering, and domain-specific information retrieval.
  • It can range from being a small Domain-Specific LLM to being a large Domain-Specific LLM.
  • It can be fine-tuned on domain-specific datasets to improve its accuracy and relevance for specific tasks.
  • It can leverage specialized vocabulary and terminologies that are unique to its domain.
  • It can serve as an expert assistant, aiding professionals in fields such as finance, law, healthcare, and science by providing domain-relevant insights and information.
  • It can utilize advanced architectures like transformers, with domain-specific enhancements to improve performance on specialized tasks.
  • It can be integrated into domain-specific applications, such as financial analysis tools, legal research platforms, and scientific discovery systems.
• Example(s):
  • a Financial-Domain LLM (for a financial domain), 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 (for a legal domain), such as:
    • LegalBERT/LEGAL-BERT, optimized for legal text processing.
    • CaseLaw LLM, designed for legal case analysis and precedent retrieval.
    • SaulLM-7B, a large language model tailored for the legal domain.
    • ...
  • a Scientific-Domain LLM (for a scientific domain), such as:
    • Galactica LLM, a scientific LLM trained on a vast array of scientific literature and data.
    • BioGPT, specialized for biomedical texts and tasks.
    • ChemBERTa, focused on chemical texts and molecular data.
    • ProteinBERT, for understanding protein sequences and structures.
    • ...
  • a Healthcare-Domain LLM (for a healthcare domain), such as:
    • ClinicalBERT, designed to process and analyze clinical narratives.
    • MedGPT, optimized for medical literature and healthcare-related tasks.
  • an Education-Domain LLM (for an education domain), such as:
    • EduBERT, tailored for educational content and student interactions.
    • TeachAI, developed to assist in educational resource generation and personalized tutoring.
  • an Environmental-Domain LLM (for an environmental domain), such as:
    • EcoGPT, designed for environmental data analysis and sustainability reports.
    • GreenBERT, focused on climate science and environmental impact assessments.
• Counter-Example(s):
  • General-purpose LLMs like GPT-3 or BERT, which are not specialized for any particular domain.
• See: BloombergGPT, FinGPT, Healthcare LLM, Legal Domain LLM.

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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.

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