提示简史：利用语言模型（通过高级提示技术）
A Brief History of Prompt: Leveraging Language Models. (Through Advanced Prompting)
AI总结：
本文系统梳理了从早期语言模型到2023年提示工程的演变历程，涵盖注意力机制、强化学习、上下文提示和预训练等关键技术，并强调伦理考量对未来AI系统的重要性。
摘要：
本文对自然语言处理（NLP）领域中提示工程与提示生成的演变进行了全面探索。从早期的语言模型和信息检索系统出发，我们追溯了多年来塑造提示工程的关键发展。2015年注意力机制的引入彻底改变了语言理解，推动了可控性和上下文感知能力的进步。随后强化学习技术的突破进一步增强了提示工程，解决了曝光偏差和生成文本中的偏见等问题。我们考察了2018年和2019年的重要贡献，重点关注微调策略、控制代码和基于模板的生成。本文还讨论了公平性、人机协作以及低资源适配日益增长的重要性。在2020年和2021年，上下文提示和迁移学习变得突出，而2022年和2023年则见证了无监督预训练和新型奖励塑形等先进技术的出现。贯穿全文，我们引用了具体的研究工作，以例证各种发展对提示工程的影响。提示工程的旅程仍在继续，伦理考量对于构建负责任且包容的AI系统未来至关重要。
英文摘要：
This paper presents a comprehensive exploration of the evolution of prompt engineering and generation in the field of natural language processing (NLP). Starting from the early language models and information retrieval systems, we trace the key developments that have shaped prompt engineering over the years. The introduction of attention mechanisms in 2015 revolutionized language understanding, leading to advancements in controllability and context-awareness. Subsequent breakthroughs in reinforcement learning techniques further enhanced prompt engineering, addressing issues like exposure bias and biases in generated text. We examine the significant contributions in 2018 and 2019, focusing on fine-tuning strategies, control codes, and template-based generation. The paper also discusses the growing importance of fairness, human-AI collaboration, and low-resource adaptation. In 2020 and 2021, contextual prompting and transfer learning gained prominence, while 2022 and 2023 witnessed the emergence of advanced techniques like unsupervised pre-training and novel reward shaping. Throughout the paper, we reference specific research studies that exemplify the impact of various developments on prompt engineering. The journey of prompt engineering continues, with ethical considerations being paramount for the responsible and inclusive future of AI systems.
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。
