Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …

Deep learning--based models have surpassed classical machine learning--based
approaches in various text classification tasks, including sentiment analysis, news …

The introduction of ChatGPT has garnered widespread attention in both academic and
industrial communities. ChatGPT is able to respond effectively to a wide range of human …

Deep learning models are often trained on distributed, web-scale datasets crawled from the
internet. In this paper, we introduce two new dataset poisoning attacks that intentionally …

Abstract The Visual Question Answering (VQA) task aspires to provide a meaningful testbed
for the development of AI models that can jointly reason over visual and natural language …

Transfer learning–ie, further fine-tuning a pre-trained model on a downstream task–can
confer significant advantages, including improved downstream performance, faster …

We introduce MetaICL (Meta-training for In-Context Learning), a new meta-training
framework for few-shot learning where a pretrained language model is tuned to do in …

Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …

Abstract Natural Language Processing (NLP) is a branch of artificial intelligence that
involves the design and implementation of systems and algorithms able to interact through …

Despite the success, the process of fine-tuning large-scale PLMs brings prohibitive
adaptation costs. In fact, fine-tuning all the parameters of a colossal model and retaining …