关于使用大型语言模型为开放式问题生成解决方案实现的研究综述与讨论
An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems
AI总结：
本文综述了利用大型语言模型为开放式问题自动生成解决方案实现的研究现状，涵盖模型提示、强化学习与检索增强生成等方法，并探讨了未来研究方向。
摘要：
大型语言模型为设计自动化实现生成方法提供了新的机遇，使问题求解活动能够超越传统方法的局限。传统方法需要算法规范，并且只能使用静态领域知识，例如性能指标和基础构建模块库。大型语言模型可以支持创建新方法来辅助开放式问题的问题求解活动，包括问题框架构建、探索可能的解决方案、特征细化与组合、更高级的实现评估，以及处理意外情况。本报告总结了当前关于大型语言模型的研究工作，涵盖模型提示、强化学习和检索增强生成等技术。此外，报告还讨论了未来的研究需求。
英文摘要：
Large Language Models offer new opportunities to devise automated implementation generation methods that can tackle problem solving activities beyond traditional methods, which require algorithmic specifications and can use only static domain knowledge, like performance metrics and libraries of basic building blocks. Large Language Models could support creating new methods to support problem solving activities for open-ended problems, like problem framing, exploring possible solving approaches, feature elaboration and combination, more advanced implementation assessment, and handling unexpected situations. This report summarized the current work on Large Language Models, including model prompting, Reinforcement Learning, and Retrieval-Augmented Generation. Future research requirements were also discussed.
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。
