most citedSynthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

8 citations · 13 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL20243 cited

How Does the Disclosure of AI Assistance Affect the Perceptions of Writing?

Zhuoyan Li, Chen Liang, Jing Peng +1

Recent advances in generative AI technologies like large language models have boosted the incorporation of AI assistance in writing workflows, leading to the rise of a new paradigm…

cs.HC2024

Understanding Decision Subjects' Engagement with and Perceived Fairness of AI Models When Opportunities of Qualification Improvement Exist

Meric Altug Gemalmaz, Ming Yin

We explore how an AI model's decision fairness affects people's engagement with and perceived fairness of the model if they are subject to its decisions, but could repeatedly and s…

cs.CR2024

Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks

Yichang Xu, Ming Yin, Minghong Fang +1

Recent studies have revealed that federated learning (FL), once considered secure due to clients not sharing their private data with the server, is vulnerable to attacks such as cl…

cs.HC20242 cited

Does More Advice Help? The Effects of Second Opinions in AI-Assisted Decision Making

Zhuoran Lu, Dakuo Wang, Ming Yin

AI assistance in decision-making has become popular, yet people's inappropriate reliance on AI often leads to unsatisfactory human-AI collaboration performance. In this paper, thro…

cs.CL20238 cited

Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu +1

The collection and curation of high-quality training data is crucial for developing text classification models with superior performance, but it is often associated with significan…