25 citations · 55 across the 6 of their papers we have counts for
6 papers
FELM: Benchmarking Factuality Evaluation of Large Language Models
Shiqi Chen, Yiran Zhao, Jinghan Zhang +4
Assessing factuality of text generated by large language models (LLMs) is an emerging yet crucial research area, aimed at alerting users to potential errors and guiding the develop…
FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios
I-Chun Chern, Steffi Chern, Shiqi Chen +6
The emergence of generative pre-trained models has facilitated the synthesis of high-quality text, but it has also posed challenges in identifying factual errors in the generated t…
Improving Factuality of Abstractive Summarization via Contrastive Reward Learning
I-Chun Chern, Zhiruo Wang, Sanjan Das +3
Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information. In this paper, we propose a simple but effective contrastiv…
Leveraging Self-Supervised Audio-Visual Pretrained Models to Improve Vocoded Speech Intelligibility in Cochlear Implant Simulation
Richard Lee Lai, Jen-Cheng Hou, I-Chun Chern +6
Individuals with hearing impairments face challenges in their ability to comprehend speech, particularly in noisy environments. The aim of this study is to explore the effectivenes…
Audio-Visual Speech Enhancement and Separation by Utilizing Multi-Modal Self-Supervised Embeddings
I-Chun Chern, Kuo-Hsuan Hung, Yi-Ting Chen +5
AV-HuBERT, a multi-modal self-supervised learning model, has been shown to be effective for categorical problems such as automatic speech recognition and lip-reading. This suggests…
Decoding of Quantum Data-Syndrome Codes via Belief Propagation
Kao-Yueh Kuo, I-Chun Chern, Ching-Yi Lai
Quantum error correction is necessary to protect logical quantum states and operations. However, no meaningful data protection can be made when the syndrome extraction is erroneous…