下图是 in-context learning (左边一列)和一般 fine-tuning (右边一列)的区别,in-context learning 不产生梯度、不会更新模型参数,而 fine-tuning 会产生梯度、更新模型参数。 需要注意区分 in-context learning 中可以有 Zero-Shot、One-Shot 和 Few-Shot 的 Setting,但和 Zero-Shot learning、One-Shot learnin...
Few-shot In-Context Preference Learning Using Large Language Models: Environment Details by Language Models (dot tech)December 4th, 2024 Too Long; Didn't Read This section presents environment details for 9 tasks in IsaacGym, including observation and action dimensions, task descriptions, and...
这样子一看,感觉上跟few-shot learning 的结果是类似的,只是不需要重新训练模型了。 论文链接 arxiv.org/abs/2310.1591 ICL简要介绍 大模型的ICL过程,也被称为情景学习和上下文学习,该过程的一个显著特性是其可以从少量的示例集合中学习新规则,并且泛化到新的查询样本中。例如,我们给定一些输入示例:“Apple->Red、...
表 2 显示了在六个分类数据集上 ZSL（ Zero-Shot Learning ）、ICL 和微调（FT）设置中的验证精度。与 ZSL 相比，ICL 和微调都取得了相当大的改进，这意味着所做的优化都有助于这些下游任务。此外，该研究发现 ICL 在 Few-shot 场景中比微调更好。表 3 中显示了 6 个数据集上 2 个 GPT 模型的 Rec2F...
Few-shot learning,允许输入数条示例和一则任务说明; One-shot learning,只允许输入一条示例和一则任务说明; Zero-shot learning,不允许输入任何示例,只允许输入一则任务说明。 忽略大模型的贵,这个范式具备不少优势: 输入的形式是自然语言,可以让我们可以更好地跟语言模型交互,通过修改模版和示例说明我们想要什么,...
Improving In-Context Few-Shot Learning via Self-Supervised Training Mingda Chen, Jingfei Du, Ramakanth Pasunuru, Todor Mihaylov, Srini Iyer, Ves Stoyanov, Zornitsa Kozareva 2022 Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
Zero-shot learning,不允许输入任何示例,只允许输入一则任务说明。 One-shot learning,只允许输入一条示例和一则任务说明。 Few-shot learning,区别于小样本学习概念,无需更新参数,允许输入数条示例和一则任务说明。 2.ICL 两个阶段的优化方法 ICL 分精调和推断两个优化方法阶段: ...
ReadWriteLoginSignUp208 reads Few-shot In-Context Preference Learning Using Large Language Models: Full Prompts and ICPL Details by Language Models (dot tech)December 3rd, 2024
To handle questions over diverse KBQA datasets with a unified training-free framework, we propose KB-BINDER, which for the first time enables few-shot in-context learning over KBQA tasks. Firstly, KB-BINDER leverages large language models like Codex to generate logical forms as the draft for a...
We study the problem of in-context learning (ICL) with large language models (LLMs) on private datasets. This scenario poses privacy risks, as LLMs may leak or regurgitate the private examples demonstrated in the prompt. We propose a novel algorithm that generates s...