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- 万字论述 OpenAI 最新的《The Prompt Report》，2024 年 . . .
文章浏览阅读2 1k次，点赞9次，收藏20次。2024 OpenAI Prompt技术报告万字总结_the prompt report 本文介绍了大型语言模型（LLM）在Prompt Engineering领域的最新技巧和发展趋势。文章分为12个部分，重点探讨了零样本提示（Zero-Shot Prompting）和少样本提示（Few-Shot Prompting）等技术，这些技术通过巧妙设计的提示
- Effective Prompt: 编写高质量Prompt的14个有效方法 - 知乎
之前提到的Prompt都属于Zero-shot Prompt，对于绝大部分任务来说，Zero-shot Prompt基本都能取得不错的效果。 但是，当任务比较困难，或者任务本身不太好下定义时，我们可以尝试使用Few-shot Prompt为大模型提供一个答案的参考框架，通过模仿的方式来生成我们期望的结果。
- 大模型幻觉缓解技术综述：A Comprehensive Survey of . . .
Prompt tuning is a technique that involves adjusting the instructions provided to a pre-trained LLM during the fine-tuning phase to make the model more effective at specific tasks The LLM learns from 'Soft Prompts', which are not predetermined but are instead learned by the model through backpropagation during the fine-tuning (Lester et al , 2021)
- A Comprehensive Survey of Hallucination Mitigation Techniques in Large . . .
For hallucination mitigation, the following techniques, which involve prompt tuning, have been proposed as of now: Universal Prompt Retrieval for Improving zero-Shot Evaluation (UPRISE): Cheng et al ( 2023 ) propose UPRISE, which tunes a lightweight and versatile retriever that automatically retrieves prompts for a given zero-shot task input
- arXiv:2407. 05474v1 [cs. AI] 7 Jul 2024
hallucination within the prompt, ensuring a more authentic and unbiased generation process 1 For the rewriting LLM, we selected GPT-4 2 due to its robust capabilities in text rewriting (Madaan et al , 2024) Leveraging a powerful rewriting LLM like GPT-4 enables the exploration of a wider array of hallucination categories, thereby enhancing the
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