The Power of Scale for Parameter-Efficient Prompt Tuning 这篇文章的soft-prompt的训练方法是只在输入层加入一些隐层向量来调整模型的表现。其贡献如下: 探索了只在输入层添加soft-prompt的方法在不同的模型(T5)规模下的表现 探索了包括prompt长度,prompt初始化方式,模型的大小,模型预训练与下游任务的差异程度对sof...
6.prompt集成 7.可解释性 原文地址 1.引言 随着预训练LLM的成功,多种技术被开发出来以便将这些通用模型应用到具体的下游任务中。例如,ELMo 提出了一种方法,保持预训练模型不变,仅学习针对其每层输出的任务特定权重。但从 GPT 和BERT 开始,最常用的适配方式变成了模型微调(Fine Tuning),即在任务适配过程中调整所...
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To address these issues, we introduce a novel prompt tuning technique that employs a hierarchical, multi-granularity prompt design. Our approach integrates remote sensing ship priors through bias terms, learned from a small trainable network. This strategy enhances the model's generalization capabilities...
We consider some relatively simple and cost-efficient ER prompt engineering methods and apply them to perform product matching on two real-world datasets widely used in the community. We select two well-known e-commerce datasets and provide extensive experimental results to show that an LLM like ...
简介:Prompt Tuning和Prefix Tuning是两种不同的预训练模型微调方法。Prompt Tuning通过在输入嵌入前添加提示来表示任务,而Prefix Tuning则是在每个transformer层以及输入层预先设置一系列前缀。本文将详细介绍这两种方法的特点和差异,并通过实验数据来分析它们的性能表现。
EPTML (Efficient Prompt Tuning within Meta-Learning framework) is an improved (speed & accuracy) method based on the previous PBML code (https://github.com/MGHZHANG/PBML) for few-shot text classification.DatasetFewRelA dataset for few-shot relation classification, containing 100 relations. Each...
翻译结果1复制译文编辑译文朗读译文返回顶部 Advantages of four complementary integration, professional investigation, a number of programmes, efficient and prompt 翻译结果2复制译文编辑译文朗读译文返回顶部 Advantages of four complementary integration, professional investigation, a number of programmes, efficient an...
[ICRA 2024] Official Implementation of the Paper "Parameter-efficient Prompt Learning for 3D Point Cloud Understanding" - auniquesun/PPT
LLMs之PE:《Efficient and Accurate Prompt Optimization: the Benefit of Memory in Exemplar-Guided Reflection》翻译与解读 导读:这篇论文的核心主题是高效准确的提示词优化,旨在提升大型语言模型 (LLM) 的生成质量。 >> 背景痛点:现有的基于反馈的自动提示词工程方法存在两个主要缺点: ...