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20232026
most citedAutomating Intervention Discovery from Scientific Literature: A Progressive Ontology Prompting and Dual-LLM Framework

2 citations · 6 across the 11 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.SD2024

Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge

Ruiyang Qin, Dancheng Liu, Gelei Xu +7

The combination of Large Language Models (LLM) and Automatic Speech Recognition (ASR), when deployed on edge devices (called edge ASR-LLM), can serve as a powerful personalized ass…

eess.AS2024★ 1 cited

Automatic Screening for Children with Speech Disorder using Automatic Speech Recognition: Opportunities and Challenges

Dancheng Liu, Jason Yang, Ishan Albrecht-Buehler +5

Speech is a fundamental aspect of human life, crucial not only for communication but also for cognitive, social, and academic development. Children with speech disorders (SD) face…

cs.CL2024★ 2 cited

Automating Intervention Discovery from Scientific Literature: A Progressive Ontology Prompting and Dual-LLM Framework

Yuting Hu, Dancheng Liu, Qingyun Wang +5

Identifying effective interventions from the scientific literature is challenging due to the high volume of publications, specialized terminology, and inconsistent reporting format…

cs.CL2024★ 2 cited

Large Language Models have Intrinsic Self-Correction Ability

Dancheng Liu, Amir Nassereldine, Ziming Yang +8

Large language models (LLMs) have attracted significant attention for their exceptional abilities in various natural language processing tasks, but they suffer from hallucinations…

cs.LG2024

FL-NAS: Towards Fairness of NAS for Resource Constrained Devices via Large Language Models

Ruiyang Qin, Yuting Hu, Zheyu Yan +3

Neural Architecture Search (NAS) has become the de fecto tools in the industry in automating the design of deep neural networks for various applications, especially those driven by…