大模型让代码生成突破固定算法限制，自动解决开放问题。
An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems
- 用提示工程、强化学习和检索增强生成实现自动化方案
- 可处理问题定义、思路探索、组合创新等开放任务
- 适合研究智能编程与自动化系统设计的开发者
大语言模型为突破传统方法局限提供了新可能，传统方法依赖预设算法和静态领域知识（如性能指标、基础组件库），而大模型可支持开放问题求解中的多个环节：问题建模、探索多种解法路径、特征细化与组合、高级实现评估以及应对意外情况。本文综述了当前在提示工程、强化学习和检索增强生成方面的进展，并讨论了未来研究方向。
原文摘要 · Abstract (English)
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.
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