Large Language Models (LLMs) like GPT-4 have become central players in the field of Natural Language Processing (NLP) due to their remarkable text generation capabilities, sentiment analysis, language translation, and various other tasks. These models are trained on vast datasets, thus possessing a wide breadth of knowledge, and hold the potential to transform our interaction with language dramatically.
However, LLMs are largely generalists, not specialists, as they possess a broad but not necessarily deep understanding of all topics. For an LLM to become a domain expert, it generally requires context management or fine-tuning, which entails specific training in a particular field.
Moreover, organizations often face challenges in fully harnessing LLMs' potential due to data siloing, a situation where data is isolated or compartmentalized across different departments or systems. This fragmentation can make it difficult for LLMs to access and learn from the complete range of available data, thereby limiting their effectiveness and potential contributions to the organization.