Closed newtonysls closed 2 years ago
@newtonysls Hi,
GPT Understands, too中的BoolQ/CB实验,和P-tuning v2中的SuperGLUE实验的setting不一样。在GPT Understands, too中,我们同时微调了backbone模型参数;而v2中,backbone模型的参数是fixed的。具体细节在两篇论文中实验部分均有详细陈述。
font{
line-height: 1.6;
}
ul,ol{
padding-left: 20px;
list-style-position: inside;
}
请问这里不是写了固定参数吗
***@***.***
在2022年3月18日 ***@***.***> 写道:
@newtonysls Hi,
GPT Understands, too中的BoolQ/CB实验,和P-tuning v2中的SuperGLUE实验的setting不一样。在GPT Understands, too中,我们同时微调了backbone模型参数;而v2中,backbone模型的参数是fixed的。具体细节在两篇论文中实验部分均有详细陈述。
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@newtonysls Hi,
抱歉,似乎你的回复是通过邮件发来的,我在github issue里看不到你引用的部分。
我可以为您指出我们原文中描述这部分的内容。在GPT Understands, Too的:
In another NLU benchmark, SuperGlue, we jointly apply the P-tuning and fine-tuning in both few-shot and fully supervised scenarios.
P-tuning puts initial prompt embeddings in different positions within patterns and then finetunes the prompt embeddings together with the pretrained models.
对于Knowledge Probing,我们则是跟随baseline方法采用的frozen setting。
font{
line-height: 1.6;
}
ul,ol{
padding-left: 20px;
list-style-position: inside;
}
感谢 可是能我看的比较粗糙 直接看方法和效果去了,没在意实验细节
***@***.***
在2022年3月20日 ***@***.***> 写道:
@newtonysls Hi,
抱歉,似乎你的回复是通过邮件发来的,我在github issue里看不到你引用的部分。
我可以为您指出我们原文中描述这部分的内容。在GPT Understands, Too的:
原文第2页(Introduction部分),有如下的说明:
In another NLU benchmark, SuperGlue, we jointly apply the P-tuning and fine-tuning in both few-shot and fully supervised scenarios.
原文第5页(Section 4.2),有如下的说明:
P-tuning puts initial prompt embeddings in different positions within patterns and then finetunes the prompt embeddings together with the pretrained models.
对于Knowledge Probing,我们则是跟随baseline方法采用的frozen setting。
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font{
line-height: 1.6;
}
ul,ol{
padding-left: 20px;
list-style-position: inside;
}
疏忽大意了哈 我就感觉v2多了那么多参数怎么还比不过v1 还是膜拜一下大佬
***@***.***
在2022年3月18日 ***@***.***> 写道:
@newtonysls Hi,
GPT Understands, too中的BoolQ/CB实验,和P-tuning v2中的SuperGLUE实验的setting不一样。在GPT Understands, too中,我们同时微调了backbone模型参数;而v2中,backbone模型的参数是fixed的。具体细节在两篇论文中实验部分均有详细陈述。
—Reply to this email directly, view it on GitHub, or unsubscribe.Triage notifications on the go with GitHub Mobile for iOS or Android.
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为什么在GPT understand too中的,在bert-large模型上,P-tuning在BoolQ数据集要比P-tuningv2高了接近三个点,包括CB数据集也是,为什么在论文中不和P-tuning进行比较呢?