Recent language models have shown impressive multilingual performance, even when not
explicitly trained for it. Despite this, there are concerns about the quality of their outputs …

Large Language Models pre-trained with self-supervised learning have demonstrated
impressive zero-shot generalization capabilities on a wide spectrum of tasks. In this work …

While large language models (LLMs) have showcased impressive capabilities, they struggle
with addressing legal queries due to the intricate complexities and specialized expertise …

Pre-trained language models (PLMs) aim to assist computers in various domains to provide
natural and efficient language interaction and text processing capabilities. However, recent …

In many real natural language processing application scenarios, practitioners not only aim to
maximize predictive performance but also seek faithful explanations for the model …

Pre-trained language models (PLMs) have demonstrated success in Chinese natural
language processing (NLP) tasks by acquiring high-quality representations through …

Pre-trained models (PTMs) have achieved great success in various Software Engineering
(SE) downstream tasks following the``pre-train then fine-tune''paradigm. As fully fine-tuning …

As the labeling costs in legal artificial intelligence tasks are expensive. Therefore, it
becomes a challenge to utilize low cost to train a robust model. In this paper, we propose a …

The widespread use of pre-trained language models (PLMs) in natural language processing
(NLP) has greatly improved performance outcomes. However, these models' vulnerability to …

Non-normative texts and euphemisms are widely spread on the Internet, making it more
difficult to moderate the content. These phenomena result from misspelling errors or …