In this work, we introduce PTMGPT2, an interpretable protein language model that utilizes prompt-based fine-tuning to improve its accuracy in precisely predicting PTMs. Drawing inspiration from recent advancements in GPT-based architectures, PTMGPT2 adopts unsupervised learning to identify PTMs. It ...
[4] Fine-tune之后的NLP新范式:Prompt越来越火,CMU华人博士后出了篇综述文章 [5] 刘鹏飞:近代自然语言处理技术发展的“第四范式” [6] 必须要GPT3吗?不,BERT的MLM模型也能小样本学习 - 科学空间|Scientific Spaces [7] P-tuning:自动构建模版,释放语言模型潜能 - 科学空间|Scientific Spaces 以上为本文的全部...
1. 在小样本的情况下,prompt-based tuning远远超过fine-tuning方法。当有足够训练数据时,fine-tuning模型也能够产生相当的结果。 2. ProtoVerb + ManualVerb方法结合时,能够产生更好的结果。 3. ProtoVerb+在untuned PLMs上仍然非常有效,证明了这种no-tuning method的有效性。 3. Analysis 3.1 Fixed Model Experim...
In this paper, we reformulate the relation extraction task as mask language modeling and propose a novel adaptive prompt-based finetuning approach. We propose an adaptive label words selection mechanism that scatters the relation label into variable number of label tokens to handle the complex multip...
This is a paper list about prompt-based tuning for large-scale pre-trained language models. Different from traditional fine-tuning that uses an explicit classifier, prompt-based tuning directly uses the pre-trained models to conduct the pre-training tasks for classification or regression....
Different from traditional fine-tuning that uses an explicit classifier, prompt-based tuning directly uses the pre-trained models to conduct the pre-training tasks for classification or regression. Keywords Convention The abbreviation of the work. The key features in terms of prompt learning used in...
Prompt-tuning提出很好的解决了pre-training和fine-tuning之间的gap,其根据设计的prompt实现下游任务的预测。目前人工构建的prompt成本较高; 三、方法 3.1 任务描述 ,三个sub-task的输出分别表示为: 本文提出模型如下图: 其主要包括两个部分: SentiPrompt tuning:在给定输入句子以及aspect以及opinion的前提下,设计continu...
(Brown et al. 2020) which does not require gradient-based fine-tuning but instead uses a few examples in the LM context as the only source of learning. In this paper, we explore prompt-based few-shot learning in dialogue tasks. We benchmark LMs of different sizes in nine response ...
2.2 Prompt-Based Learning vs. Fine-Tuning prompt-based learning 不需要训练,模型稳定,不需要更新。 语言模型 我们分析比较不同大小的单向语言模型: GPT-2 (Radford et al., 2019)四种尺寸、(0.1B, 0.3B, 0.8B, 1.6B), GPT_neo (Black et al., 2021),两种尺寸(1.3B, 2.7B)和6B GPT - j (Wang...
这就是一个使用同义词预训练soft prompt的方法;这个训练过程使用与下游任务类似的语料;这个过程只训练一次,最后在下游少样本有标注数据上fine-tuning一下即可。 Dual-View Data Augmentation 作者设计了一种双视图的数据方式,即:输入视图和输出视图。输入视图基于关键词生成合成的数据;输出视图则基于输出的标签。上面的...