Large Language Models are Pattern Matchers: Editing Semi-Structured and Structured Documents with ChatGPT
This work addresses the problem of automating document editing for users, but it is incremental as it applies existing methods to new data.
The paper investigated whether large language models (LLMs) like ChatGPT can edit structured and semi-structured documents with minimal effort, finding that they effectively edit such documents when given basic prompts and demonstrate strong pattern matching skills.
Large Language Models (LLMs) offer numerous applications, the full extent of which is not yet understood. This paper investigates if LLMs can be applied for editing structured and semi-structured documents with minimal effort. Using a qualitative research approach, we conduct two case studies with ChatGPT and thoroughly analyze the results. Our experiments indicate that LLMs can effectively edit structured and semi-structured documents when provided with basic, straightforward prompts. ChatGPT demonstrates a strong ability to recognize and process the structure of annotated documents. This suggests that explicitly structuring tasks and data in prompts might enhance an LLM's ability to understand and solve tasks. Furthermore, the experiments also reveal impressive pattern matching skills in ChatGPT. This observation deserves further investigation, as it may contribute to understanding the processes leading to hallucinations in LLMs.