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Chengfei Li

2 accepted papers

2024

Leveraging Local Variance for Pseudo-Label Selection in Semi-supervised Learning

AAAI 2024technical

Semi-supervised learning algorithms that use pseudo-labeling have become increasingly popular for improving model performance by utilizing both labeled and unlabeled data. In this paper, we offer a fresh perspective on the selection of pseudo-labels, inspired by theoretical insights. We suggest tha…

Cited by 3SourcePDFScholar
2023

Unveiling the Implicit Toxicity in Large Language Models

EMNLP 2023long main

The open-endedness of large language models (LLMs) combined with their impressive capabilities may lead to new safety issues when being exploited for malicious use. While recent studies primarily focus on probing toxic outputs that can be easily detected with existing toxicity classifiers, we show t…

Cited by 0SourcecodeScholar