12 citations · 27 across the 12 of their papers we have counts for
7 papers
Less than One-shot: Named Entity Recognition via Extremely Weak Supervision
Letian Peng, Zihan Wang, Jingbo Shang
We study the named entity recognition (NER) problem under the extremely weak supervision (XWS) setting, where only one example entity per type is given in a context-free way. While…
Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models
An Yan, Yu Wang, Yiwu Zhong +8
Medical image classification is a critical problem for healthcare, with the potential to alleviate the workload of doctors and facilitate diagnoses of patients. However, two challe…
RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
Jiongnan Liu, Jiajie Jin, Zihan Wang +3
Although Large Language Models (LLMs) have demonstrated extraordinary capabilities in many domains, they still have a tendency to hallucinate and generate fictitious responses to u…
A Benchmark on Extremely Weakly Supervised Text Classification: Reconcile Seed Matching and Prompting Approaches
Zihan Wang, Tianle Wang, Dheeraj Mekala +1
Etremely Weakly Supervised Text Classification (XWS-TC) refers to text classification based on minimal high-level human guidance, such as a few label-indicative seed words or class…
M^4I: Multi-modal Models Membership Inference
Pingyi Hu, Zihan Wang, Ruoxi Sun +2
With the development of machine learning techniques, the attention of research has been moved from single-modal learning to multi-modal learning, as real-world data exist in the fo…
Rethinking the Setting of Semi-supervised Learning on Graphs
Ziang Li, Ming Ding, Weikai Li +4
We argue that the present setting of semisupervised learning on graphs may result in unfair comparisons, due to its potential risk of over-tuning hyper-parameters for models. In th…