5 papers
Next Generation Active Learning: Mixture of LLMs in the Loop
Yuanyuan Qi, Xiaohao Yang, Jueqing Lu +4
With the rapid advancement and strong generalization capabilities of large language models (LLMs), they have been increasingly incorporated into the active learning pipelines as an…
ALScope: A Unified Toolkit for Deep Active Learning
Chenkai Wu, Yuanyuan Qi, Xiaohao Yang +4
Deep Active Learning (DAL) reduces annotation costs by selecting the most informative unlabeled samples during training. As real-world applications become more complex, challenges…
DPL: Decoupled Prototype Learning for Enhancing Robustness of Vision-Language Transformers to Missing Modalities
Jueqing Lu, Yuanyuan Qi, Xiaohao Yang +8
The performance of Visio-Language Transformers drops sharply when an input modality (e.g., image) is missing, because the model is forced to make predictions using incomplete infor…
CGMatch: A Different Perspective of Semi-supervised Learning
Bo Cheng, Jueqing Lu, Yuan Tian +3
Semi-supervised learning (SSL) has garnered significant attention due to its ability to leverage limited labeled data and a large amount of unlabeled data to improve model generali…
Neural Topic Modeling with Large Language Models in the Loop
Xiaohao Yang, He Zhao, Weijie Xu +4
Topic modeling is a fundamental task in natural language processing, allowing the discovery of latent thematic structures in text corpora. While Large Language Models (LLMs) have d…