3 papers
cs.CL2025
PromptAL: Sample-Aware Dynamic Soft Prompts for Few-Shot Active Learning
Hui Xiang, Jinqiao Shi, Ting Zhang +3
Active learning (AL) aims to optimize model training and reduce annotation costs by selecting the most informative samples for labeling. Typically, AL methods rely on the empirical…
cs.CL2024
A Novel Prompt-tuning Method: Incorporating Scenario-specific Concepts into a Verbalizer
Yong Ma, Senlin Luo, Yu-Ming Shang +2
The verbalizer, which serves to map label words to class labels, is an essential component of prompt-tuning. In this paper, we present a novel approach to constructing verbalizers.…
cs.CL2024
Enhancing Source Code Classification Effectiveness via Prompt Learning Incorporating Knowledge Features
Yong Ma, Senlin Luo, Yu-Ming Shang +2
Researchers have investigated the potential of leveraging pre-trained language models, such as CodeBERT, to enhance source code-related tasks. Previous methodologies have relied on…