Large Language Models are Pattern Matchers: Editing Semi-Structured and Structured Documents with ChatGPT

Large Language Models (LLMs) are extensive artificial neural networks trained on vast amounts of textual data to generate coherent continuations of given prompts. The initial training, which is time-consuming and computationally intensive, is typically followed by additional training phases. Fine-tuning with specific tasks and example responses enables LLMs to solve particular types of problems, while Reinforcement Learning with Human Feedback focuses them on delivering high-quality and socially preferred responses. Research has shown that LLMs can not only produce correct natural and formal language texts conveying plausible contents, but are also capable of reasoning, planning, and simulating other forms of intelligent behaviors. Thus, LLMs offer a wide range of potential applications, the extent of which is still not fully explored. Frequently, LLMs are applied for creating and processing texts, for communicating, planning, and computer programming. LLMs require that all tasks and inputs are provided in a textual format. For many applications, LLMs are prompted with freely phrased, natural language text or program code. Yet, they are also capable of processing texts that are structured such that they represent data or formatted documents.