2 papers
cs.LG2026
FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices
Changyu Li, Shuanghong Huang, Jiashen Liu +5
Federated fine-tuning provides a practical route to adapt large language models (LLMs) on edge devices without centralizing private data. However, in mobile deployments, the traini…
cs.CL2026
Pardon? Evaluating Conversational Repair in Large Audio-Language Models
Shuanghong Huang, Jinlei Xu, Youchao Zhou +4
Large Audio-Language Models (LALMs) have demonstrated strong performance in spoken question answering (QA), with existing evaluations primarily focusing on answer accuracy and robu…