The prompt-based learning paradigm, which bridges the gap between pre-training and fine-
tuning, achieves state-of-the-art performance on several NLP tasks, particularly in few-shot …

In recent years, prompt tuning has proven effective in adapting pre-trained vision-language
models to down-stream tasks. These methods aim to adapt the pre-trained models by …

The spread of rumors along with breaking events seriously hinders the truth in the era of
social media. Previous studies reveal that due to the lack of annotated resources, rumors …

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 …

Recent research showed promising results on combining pretrained language models (LMs)
with canonical utterance for few-shot semantic parsing. The canonical utterance is often …

While large language models (LLMs), such as GPT-3, appear to be robust and general, their
reasoning ability is not at a level to compete with the best models trained for specific natural …

A common problem with adopting Text-to-SQL translation in database systems is poor
generalization. Specifically, when there is limited training data on new datasets, existing few …

Recent work has shown that generation from a prompted or fine-tuned language model can
perform well at semantic parsing when the output is constrained to be a valid semantic …

Recent advances in deep learning have greatly propelled the research on semantic parsing.
Improvement has since been made in many downstream tasks, including natural language …

Creating layouts is a fundamental step in graphic design. In this work, we propose to use text
as the guidance to create graphic layouts, ie, Text-to-Layout, aiming to lower the design …