3 citations · 10 across the 17 of their papers we have counts for
11 papers · 1 filter
NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs
Ruiyang Qin, Pengyu Ren, Zheyu Yan +7
Large Language Models (LLMs) deployed on edge devices, known as edge LLMs, need to continuously fine-tune their model parameters from user-generated data under limited resource con…
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…
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…
Towards Precision Characterization of Communication Disorders using Models of Perceived Pragmatic Similarity
Nigel G. Ward, Andres Segura, Georgina Bugarini +4
The diagnosis and treatment of individuals with communication disorders offers many opportunities for the application of speech technology, but research so far has not adequately c…
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…
FASA: a Flexible and Automatic Speech Aligner for Extracting High-quality Aligned Children Speech Data
Dancheng Liu, Jinjun Xiong
Automatic Speech Recognition (ASR) for adults' speeches has made significant progress by employing deep neural network (DNN) models recently, but improvement in children's speech i…
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…
PI-Whisper: Designing an Adaptive and Incremental Automatic Speech Recognition System for Edge Devices
Amir Nassereldine, Dancheng Liu, Chenhui Xu +3
Edge-based automatic speech recognition (ASR) technologies are increasingly prevalent in the development of intelligent and personalized assistants. However, resource-constrained A…
Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices
Ruiyang Qin, Dancheng Liu, Chenhui Xu +9
The scaling laws have become the de facto guidelines for designing large language models (LLMs), but they were studied under the assumption of unlimited computing resources for bot…
Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures
Ruiyang Qin, Zheyu Yan, Dewen Zeng +8
Large Language Models (LLMs) deployed on edge devices learn through fine-tuning and updating a certain portion of their parameters. Although such learning methods can be optimized…
Ensembler: Protect Collaborative Inference Privacy from Model Inversion Attack via Selective Ensemble
Dancheng Liu, Chenhui Xu, Jiajie Li +2
For collaborative inference through a cloud computing platform, it is sometimes essential for the client to shield its sensitive information from the cloud provider. In this paper,…