10 citations · 17 across the 8 of their papers we have counts for
8 papers
The Importance of Human-Labeled Data in the Era of LLMs
Yang Liu
The advent of large language models (LLMs) has brought about a revolution in the development of tailored machine learning models and sparked debates on redefining data requirements…
How Does In-Context Learning Help Prompt Tuning?
Simeng Sun, Yang Liu, Dan Iter +2
Fine-tuning large language models is becoming ever more impractical due to their rapidly-growing scale. This motivates the use of parameter-efficient adaptation methods such as pro…
Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information
Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim +6
This work focuses on in-context data augmentation for intent detection. Having found that augmentation via in-context prompting of large pre-trained language models (PLMs) alone do…
Attend to the Right Context: A Plug-and-Play Module for Content-Controllable Summarization
Wen Xiao, Lesly Miculicich, Yang Liu +2
Content-Controllable Summarization generates summaries focused on the given controlling signals. Due to the lack of large-scale training corpora for the task, we propose a plug-and…
Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models
Chengcheng Ma, Yang Liu, Jiankang Deng +3
Pretrained vision-language models (VLMs) such as CLIP have shown impressive generalization capability in downstream vision tasks with appropriate text prompts. Instead of designing…
Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning
Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar +3
Prefix-tuning, or more generally continuous prompt tuning, has become an essential paradigm of parameter-efficient transfer learning. Using a large pre-trained language model (PLM)…
Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention
Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar +3
The massive amount of trainable parameters in the pre-trained language models (PLMs) makes them hard to be deployed to multiple downstream tasks. To address this issue, parameter-e…
ISTRBoost: Importance Sampling Transfer Regression using Boosting
Shrey Gupta, Jianzhao Bi, Yang Liu +1
Current Instance Transfer Learning (ITL) methodologies use domain adaptation and sub-space transformation to achieve successful transfer learning. However, these methodologies, in…