Prompt Tuning比Fine-tuning在哪些情况下表现更好? 结论很简单:离散的Prompt Tuning(Prompt Design)基本不能达到fine-tuning的效果;Soft Prompt Tuning在模型增大时可以达到接近fine-tuning的效果,并且有进一步超越fine-tuning的趋势。 另外,Prompt Tuning往往比模型调优提供更强的零样本性能,尤其是在像 TextbookQA 这样具...
importosenv_file="/root/autodl-fs/01jupyter/.env"ifos.path.isfile(env_file):withopen(env_file...
自从GPT、EMLO、BERT的相继提出,以Pre-training + Fine-tuning 的模式在诸多自然语言处理(NLP)任务中被广泛使用,其先在Pre-training阶段通过一个模型在大规模无监督语料上预先训练一个 预训练语言模型(Pre-trained Language Model,PLM) ,然后在Fine-tuning阶段基于训练好的语言模型在具体的下游任务上再次进行 微调(F...
本文的目标是介绍Prompt-Tuning的方法,而Prompt-Tuning的动机则是进一步拉近微调与预训练阶段的任务目标,因此本部分则以常用的BERT为主,简单介绍Pre-training的经典方法,更加详细的解读,可参考:【预训练语言模型】BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding(BERT) [2] 。 (1)Mas...
sri-awadh / prompt-tuning Public forked from google-research/prompt-tuning Notifications You must be signed in to change notification settings Fork 0 Star 0 Original Implementation of Prompt Tuning from Lester, et al, 2021 License Apache-2.0 license 0 stars 57 forks Branches Tags ...
本文的目标是介绍Prompt-Tuning的方法,而Prompt-Tuning的动机则是进一步拉近微调与预训练阶段的任务目标,因此本部分则以常用的BERT为主,简单介绍Pre-training的经典方法,更加详细的解读,可参考:【预训练语言模型】BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding(BERT)[2]。
虽然本指南不深入探讨这个话题,但如果您对此感兴趣,以下是一些关键论文: AutoPrompt- 提出了一种基于梯度引导搜索的自动创建各种任务提示的方法。 Prefix Tuning- 一种轻量级的fine-tuning替代方案,为NLG任务准备可训练的连续前缀。 Prompt Tuning- 提出了一种通过反向传播学习软提示的机制。
The data from this feedback can also feed into examples for fine-tuning, which starts to beat prompt engineering once you can supply a few thousand examples, as shown in Figure 1-13. Figure 1-13. How many data points is a prompt worth? Graduating from thumbs-up or thumbs-down, you ...
What is it? SPTAR representsSoft Prompt Tuning for Augmenting Dense Retrieval with Large Language Modelswhich consists of six modules as shown in the following image: This repo consists of two flodersxuyangandzhiyuanwherexuyangcontains the soft prompt tuning, soft prompt filter and soft prompt augme...
Before introducing PTR, we first give some essential preliminaries, especially some details about prompt tuning for classification. Formally, a classification task can be denoted as T={X,Y}, where X is the instance set and Y is the class set. For x∈X, it consists of tokens x={w1,w2,...