We've featured large language models (LLMs) like BERT and ERNIE in the Radar before; domain-specific LLMs, however, are an emerging trend. Fine-tuning general-purpose LLMs with domain-specific data can tailor them for various tasks, including information retrieval, customer support augmentation and content creation. This practice has shown promising results in industries like legal and finance, as demonstrated by OpenNyAI for legal document analysis. With more organizations experimenting with LLMs and new models like GPT4 being released, we can expect more domain-specific use cases in the near future.
However, there are challenges and pitfalls to consider. First, LLMs can be confidently wrong, so it's essential to build mechanisms into your process to ensure the accuracy of results. Second, third-party LLMs may retain and re-share your data, posing a risk to proprietary and confidential information. Organizations should carefully review the terms of use and trustworthiness of providers or consider training and running LLMs on an infrastructure they control. As with any new technology, businesses must tread carefully, understanding the implications and risks associated with LLM adoption.
