从想法到实现：评估大语言模型在软件开发中的影响——一篇观点性论文
From Idea to Implementation: Evaluating the Influence of Large Language Models in Software Development -- An Opinion Paper
AI总结：
该观点性论文收集分析11位专家使用大语言模型进行软件开发的经验，指出其可提升生产力、缩短编码时间，同时也存在过度依赖、伦理等潜在问题，为大语言模型在软件开发领域的负责任整合提供参考。
摘要：
Transformer架构的问世是自然语言处理（NLP）领域的一个转折点。基于Transformer架构的模型，如Bidirectional Encoder Representations from Transformers（BERT，双向编码器表征来自Transformer）和Generative Pre-Trained Transformer（GPT，生成式预训练Transformer），已在软件开发、教育等各类应用中得到广泛普及。ChatGPT、Bard等大语言模型（LLMs）面向公众开放，展现了这类模型的巨大潜力，也推动了它们融入软件开发等多个领域，用于代码生成、调试、文档生成等任务。本研究收集并分析了11位专家关于使用LLMs进行软件开发的经验观点，以得出可指导其成功且负责任地落地应用的洞见。专家们的整体态度积极，指出了生产力提升、编码时间缩短等优势，同时也强调了过度依赖风险、伦理考量等潜在问题与挑战。
英文摘要：
The introduction of transformer architecture was a turning point in Natural Language Processing (NLP). Models based on the transformer architecture such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT) have gained widespread popularity in various applications such as software development and education. The availability of Large Language Models (LLMs) such as ChatGPT and Bard to the general public has showcased the tremendous potential of these models and encouraged their integration into various domains such as software development for tasks such as code generation, debugging, and documentation generation. In this study, opinions from 11 experts regarding their experience with LLMs for software development have been gathered and analysed to draw insights that can guide successful and responsible integration. The overall opinion of the experts is positive, with the experts identifying advantages such as increase in productivity and reduced coding time. Potential concerns and challenges such as risk of over-dependence and ethical considerations have also been highlighted.
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。
