in context learning large language model 在上下文学习(In-context Learning)是一种训练语言模型的方法,特别适用于大规模语言模型(Large Language Models)。这种方法允许模型通过观察上下文中的信息来学习新任务,而不需要大量的示例数据。 在上下文学习中,模型被提供了一个上下文,其中包含了与目标任务相关的信息。这个...
但是研究人员发现,通过语境学习(Incontext Learning,ICL)等方法,直接使用大规模语言模型就可以在很多任务的少样本场景下取得了很好的效果。此后,研究人员们提出了面向大规模语言模型的提示词(Prompt)学习方法、模型即服务范式(Model as a Service,MaaS)、指令微调(Instruction Tuning)等方法, 2022 年底ChatGPT 的出现,...
为了让模型知道什么叫做「相似语言判断任务」,我们借用 Incontext Learning 的方式,先给模型展示几个正确的例子: >>> User: 句子一: 如何找回账号\n句子二: 账号丢了怎么办\n上面两句话是相似的语义吗? >>> Bot: 是 >>> User: 如何找回账号\n句子二: 附近最近的饭店\n上面两句话是相似的语义吗? >>> ...
Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this process is computationally and technically demanding. In language processing, in-context learning provides an alternative, where ...
Large language models (LLMs) have demonstrated their ability to learn in-context, allowing them to perform various tasks based on a few input-output examples. However, the effectiveness of in-context learning is heavily reliant on the quality of the selected examples. In this pape...
LLMs demonstrate an in-context learning (ICL) ability, that is, learning from a few examples in the context. Many studies have shown that LLMs can perform a series of complex tasks through ICL, such as solving mathematical reasoning problems.
In-context Learning (ICL) has emerged as a powerful capability alongside the development of scaled-up large language models (LLMs). By instructing LLMs using few-shot demonstrative examples, ICL enables them to perform a wide range of tasks without updating millions of parameters. However, the ...
In-context Learning A Survey for In-context Learning by Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu and Zhifang Sui and Rethinking the Role of Demonstrations: What Makes In-context Learning Work? by Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel...
Large Language Models (LLMs) have succeeded considerably in In-Context-Learning (ICL) based summarization. However, saliency is subject to the users' specific preference histories. Hence, we need reliable In-Context Personalization Learning (ICPL) capabilities within such LLMs. For any arbitrary LLM...
上下文学习(in-context learning) 提示生成技术(prompt generation),包括: 可变微调软提示技术(differentiable tuning of soft prompts) 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。