The big picture
LLMs are neural models with billions of parameters.
These models are pre-trained on extensive data and are capable of a wide range of natural language processing tasks.
With recent developments, LLMs are fast emerging as a vital enterprise technology that promises to create shifts in how businesses build, adopt, and use AI.
However, despite their promise and heightened interest among enterprises, concerns over security, risks implications, and potential societal impact remain when considering their use within an enterprise.
While there is euphoria around AI’s generative and conversational capabilities, it is important to step back and take a wider view.
Only enterprises that contextually exploit the potential of LLMs in a responsible and resilient manner will succeed in generating value from them.
With the benefit of large-scale pre-training, a defining characteristic of LLMs has been the ability to adapt existing models to new tasks and domains.
This is demonstrated in instances of code generation, answering medical-related questions, analysis of legal text, and so on.
Until recently, such domain adaptation involved selecting a pre-trained model and fine-tuning it on domain-specific data. However, the latest generation of LLMs has demonstrated unique abilities to adapt to new tasks, with just a few specific examples fed as natural language inputs through prompts.
Such developments obviate the need to build models from scratch and the need for substantial amounts of training data, both of which have been barriers to AI adoption.
With the emergence of generative models such as GPT-3, there is an increased interest in prompt engineering, with supporting technologies such as vector databases and prompt chaining that continually expand the scope of LLMs.
Alongside LLMs, prompt engineering tools and techniques have developed rapidly, enabling complex tasks beyond conversations. Techniques such as chain-of-thought prompting, which helps break complex tasks into logical steps, have improved the performance of LLMs in resolving tasks that need logical reasoning, with prompt-chaining tools making it possible to design and orchestrate multi-step workflows.
Supporting technologies that augment and extend standalone LLM capabilities, such as vector databases and plugins, are expanding. Connecting LLMs with external data and systems will help overcome inherent shortfalls and unlock new possibilities.
LLMs are fast emerging as general-purpose AI, with models becoming increasingly more capable. These will play a meaningful role in enterprise AI adoption and innovation.