大语言模型是模式匹配器：使用ChatGPT编辑半结构化和结构化文档
Large Language Models are Pattern Matchers: Editing Semi-Structured and Structured Documents with ChatGPT
AI总结：
本文通过两项ChatGPT案例研究，发现大语言模型可借助简单提示有效编辑结构化/半结构化文档，具备强结构处理与模式匹配能力，提示词结构化可提升任务表现，相关发现或助力理解LLM幻觉机制。
摘要：
大语言模型（LLMs）具备众多应用场景，其全部潜力尚未被完全探明。本文探究了LLMs能否以极低的工作量应用于结构化和半结构化文档的编辑工作。我们采用定性研究方法，以ChatGPT为对象开展了两项案例研究，并对结果进行了深入分析。实验表明，当提供基础、直白的提示词时，LLMs能够有效编辑结构化和半结构化文档。ChatGPT展现出识别和处理带标注文档结构的强大能力，这说明在提示词中明确构建任务和数据的结构，可能会提升LLM理解和解决任务的能力。此外，实验还揭示了ChatGPT出色的模式匹配能力。这一发现值得进一步研究，因为它可能有助于理解LLMs产生幻觉的过程。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
