Few-shot in-context learning (ICL) enables pre-trained language models to perform a
previously-unseen task without any gradient-based training by feeding a small number of …

Recent few-shot methods, such as parameter-efficient fine-tuning (PEFT) and pattern
exploiting training (PET), have achieved impressive results in label-scarce settings …

Designing dialog tutors has been challenging as it involves modeling the diverse and
complex pedagogical strategies employed by human tutors. Although there have been …

The strong few-shot in-context learning capability of large pre-trained language models
(PLMs) such as GPT-3 is highly appealing for application domains such as biomedicine …

Large language models (LLMs) have become phenomenally surging, since 2018--two
decades after introducing context-awareness into computing systems. Through taking into …

Pretrained large language models (LLMs) have emerged as a cornerstone in modern
natural language processing, with their utility expanding to various applications and …

Instruction tuning of language models has demonstrated the ability to enhance model
generalization to unseen tasks via in-context learning using a few examples. However …

Static analysis tools for capturing bugs and vulnerabilities in software programs are widely
employed in practice, as they have the unique advantages of high coverage and …

Natural language processing (NLP) sees rich mobile applications. To support various
language understanding tasks, a foundation NLP model is often fine-tuned in a federated …

Many natural language processing (NLP) tasks are naturally imbalanced, as some target
categories occur much more frequently than others in the real world. In such scenarios …