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 …

Abstract Named Entity Recognition (NER) is an important component of Natural Language
Processing (NLP) and is a fundamental yet challenging task in text analysis. Recently, NER …

In-context learning, a paradigm bridging the gap between pre-training and fine-tuning, has
demonstrated high efficacy in several NLP tasks, especially in few-shot settings. Despite …

Large Language Models (LLMs), which bridge the gap between human language
understanding and complex problem-solving, achieve state-of-the-art performance on …

Recently, various parameter-efficient fine-tuning (PEFT) strategies for application to
language models have been proposed and successfully implemented. However, this raises …

The engineering geology report serves as a comprehensive portrayal of the geological
conditions and information within a surveyed region, making it highly valuable for extracting …

Local feature matching involves the task of establishing the pixel-wise correspondences
between a pair of images. As an integral component of plentiful computer vision applications …

In-context learning, a paradigm bridging the gap between pre-training and fine-tuning, has
demonstrated high efficacy in several NLP tasks, especially in few-shot settings. Unlike …

Graph representation learning faces challenges in node classification tasks due to the
intricate interplay between node categories, topological structure, significant characteristics …

Prompt-based learning, a paradigm that creates a bridge between pre-training and fine-
tuning stages, has proven to be highly effective concerning various NLP tasks, particularly in …