自然语言提示工程(natural language prompt engineering):它为人类提供了一个自然的界面与机器沟通,这里的机器不仅限于LLMs,也包括诸如提示驱动的图像合成器之类的模型。 以上这些研究方向的背后,都隐含了一个事实: 因为LLMs本质是一个序列条件概率模型,简单的语言提示并不总是能产生预期的结果,输入序列的每一个微小地...
Modern models can be fine-tuned for specific tasks or guided by prompt engineering.[1] These models acquire predictive power regarding syntax, semantics, and ontologies[2] inherent in human language corpora, but they also inherit inaccuracies and biases present in the data they are trained in.[...
A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks 【要点】:本文调研了44篇关于大型语言模型(LLM)在不同自然语言处理(NLP)任务中使用提示工程方法的研究,总结出39种不同的提示工程方法,并对它们在不同NLP任务中的性能进行了分析。【方法】:本文采用文献综述的方法,将研究分...
questions, or any other type of input, depending on the intended use of the model. In the case of Stable Diffusion models, for example, the prompt is the description of the image to generate
Learn about prompt engineering - the practice of designing and optimizing the text prompts given to large language models. Discover how.
Prompt Engineering for Large Language Models such as OpenAI – GPT is a rapidly evolving area of research and engineering practice. We have found thru trial and error that generating summaries of text using GPT can be enhanced using these guidelines: ...
与LLMs进行交互的一种常见方法是提示工程(prompt engineering),用户设计并提供特定的提示文本,引导LLMs生成期望的回应或完成特定的任务。这在现有的评估工作中被广泛采用。 人们还可以进行问答交互,他们向模型提出问题并获得答案,或进行对话交互,与LLMs进行自然语言对话。
为此,Visual ChatGPT采用ChatGPT作为和用户交流的理解中枢,整合了多个视觉基础模型(Visual Foundation Models),通过prompt engineering (即Prompt Manager)告诉ChatGPT各个基础模型的用法以及输入输出格式,让LLM决定为了满足用户的需求,应该如何调用这些模型,如下图所示。
In this post, I discuss a few ways of getting around with LLMs, so that you can make the best out of them. For more information about getting started with LLMs, seeAn Introduction to Large Language Models: Prompt Engineering and P-Tuning. ...
这就是所谓的提示工程("prompt engineering"),这是你在这门课程的下一部分将要探索的。 提示和提示工程(Prompting and prompt engineering) 这段内容主要是关于使用大型语言模型(Large Language Models,简称LLMs)进行任务的一些重要术语和策略。以下是这段内容的结构化概述: ...