[HTML][HTML] Parameter-efficient fine-tuning of large-scale pre-trained language models
With the prevalence of pre-trained language models (PLMs) and the pre-training–fine-tuning
paradigm, it has been continuously shown that larger models tend to yield better …
paradigm, it has been continuously shown that larger models tend to yield better …
Black-box tuning for language-model-as-a-service
Extremely large pre-trained language models (PTMs) such as GPT-3 are usually released
as a service. It allows users to design task-specific prompts to query the PTMs through some …
as a service. It allows users to design task-specific prompts to query the PTMs through some …
Openprompt: An open-source framework for prompt-learning
Prompt-learning has become a new paradigm in modern natural language processing,
which directly adapts pre-trained language models (PLMs) to $ cloze $-style prediction …
which directly adapts pre-trained language models (PLMs) to $ cloze $-style prediction …
All in one: Multi-task prompting for graph neural networks
Recently," pre-training and fine-tuning''has been adopted as a standard workflow for many
graph tasks since it can take general graph knowledge to relieve the lack of graph …
graph tasks since it can take general graph knowledge to relieve the lack of graph …
Sparse low-rank adaptation of pre-trained language models
Fine-tuning pre-trained large language models in a parameter-efficient manner is widely
studied for its effectiveness and efficiency. The popular method of low-rank adaptation …
studied for its effectiveness and efficiency. The popular method of low-rank adaptation …
ELLE: Efficient lifelong pre-training for emerging data
Current pre-trained language models (PLM) are typically trained with static data, ignoring
that in real-world scenarios, streaming data of various sources may continuously grow. This …
that in real-world scenarios, streaming data of various sources may continuously grow. This …
Vida: Homeostatic visual domain adapter for continual test time adaptation
Since real-world machine systems are running in non-stationary environments, Continual
Test-Time Adaptation (CTTA) task is proposed to adapt the pre-trained model to continually …
Test-Time Adaptation (CTTA) task is proposed to adapt the pre-trained model to continually …
Infoprompt: Information-theoretic soft prompt tuning for natural language understanding
Soft prompt tuning achieves superior performances across a wide range of few-shot tasks.
However, the performances of prompt tuning can be highly sensitive to the initialization of …
However, the performances of prompt tuning can be highly sensitive to the initialization of …
Gradient-free textual inversion
Recent works on personalized text-to-image generation usually learn to bind a special token
with specific subjects or styles of a few given images by tuning its embedding through …
with specific subjects or styles of a few given images by tuning its embedding through …
Moderate-fitting as a natural backdoor defender for pre-trained language models
Despite the great success of pre-trained language models (PLMs) in a large set of natural
language processing (NLP) tasks, there has been a growing concern about their security in …
language processing (NLP) tasks, there has been a growing concern about their security in …