零样本泛化能力(Zero-shot Generalization)是深度学习模型,尤其是大模型的一个重要特性。它与传统的深度学习模型的泛化能力有着显著区别。 有同学在刚接触大模型的时候也许会对这个问题产生困惑,那么到底什么是零样本泛化能力呢? 1. 零样本泛化能力的定义 零样本泛化能力指的是模型在从未见过的特定任务或数据集上,仅...
Cross-Datasets Generalization Context-dependent Visual Reasoning on Bongard-HOI Ablation Study TL;DR 把CLIP用到Test-Time Adaptation(TTA)这一setting下的一篇文章,具体涉及到的任务是图像分类和context-dependent visual reasoning。实验上,图像分类任务和CLIP、CoOp、CoCoOp做了比较;context-dependent visual reasoning...
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a consequence of implicit multitask learning in language models' pretraining (Radford et al., 2019). Can zero-...
标题:What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? 文章链接:What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? 代码:bigscience-workshop/architecture-objective 发表:2022 领域:LLM 最优架构探索 一句话总结:作...
We find that zero-shot generalization occurs during the very early stage of instruction tuning, despite the metrics chosen for measurement, while loss serves as a reasonable and suitable metric to measure zero-shot generalization due to its stability and fairness across datasets. We identify two ent...
今天给大家介绍一篇由42位作者共同参与的论文《Multitask Prompted Training Enables Zero-Shot Task Generalization》这篇论文由Hugging Face牵头，如果用一连串数字来概括这篇论文，我们就会发现“大力真的可以创造奇迹”：· 一共收集了171个多任务数据集，总共创建了1939个prompt，平均每个数据集有11.3个prompt；· 共...
Multitask Prompted Training Enables Zero-Shot Task Generalization 论文链接: https://arxiv.org/abs/2110.08207 2.1 Motivation T0 和 FLAN 工作整体相似,区别是增加了任务和 prompt 数量,FLAN 使用了 decoder-only,T0 使用了 encoder+decoder,FLAN 每次针对测试一个任务训练一个模型,其他任务作为训练集,T0 为了测...
4、新任务泛化(Novel task generalization):测试时带有新提示模板的新型元任务 VIMA模型 多模态prompt中总共包含三种格式: 1、文本,使用预训练的T5模型进行分词及获取词向量; 2、整个桌面的场景,首先使用Mask R-CNN识别出所有的独立物体,每个物体由一个bounding box和裁剪图像表示,然后使用一个bounding bo编码器和ViT...
前几天,JayJay刷到一篇NB的paper《Multitask Prompted Training Enables Zero-Shot Task Generalization》,共有42位作者参与,实属巨制: 这篇论文由Hugging Face牵头,如果用一连串数字来概括这篇论文,我们就会发现“大力真的可以创造奇迹”: 一共收集了171个多任务数据集,总共创建了1939个prompt,平均每个数据集有11.3个...
BC-Z: Zero-Shot Task Generalization with Robotic Imitation LearningEric JangAlex IrpanMohi KhansariDaniel KapplerFrederik EbertCorey LynchSergey LevineChelsea Finn5th Annual Conference on Robot Learning