4 papers
A Framework for Human-AI Q-Matrix Refinement: A NeuralCDM Evaluation
Ying Zhang, Ningxi Cheng, Yizhu Gao +5
Q-matrices are a cornerstone of theory-driven assessment and learning analytics, making item demands and students' underlying knowledge components and misconceptions explicit and a…
Q-realign: Piggybacking Realignment on Quantization for Safe and Efficient LLM Deployment
Qitao Tan, Xiaoying Song, Ningxi Cheng +6
Public large language models (LLMs) are typically safety-aligned during pretraining, yet task-specific fine-tuning required for deployment often erodes this alignment and introduce…
End-to-End On-Device Quantization-Aware Training for LLMs at Inference Cost
Qitao Tan, Xiaoying Song, Jin Lu +9
Quantization is an effective technique to reduce the deployment cost of large language models (LLMs), and post-training quantization (PTQ) has been widely studied due to its effici…
Rethinking the Potential of Layer Freezing for Efficient DNN Training
Chence Yang, Ci Zhang, Lei Lu +11
With the growing size of deep neural networks and datasets, the computational costs of training have significantly increased. The layer-freezing technique has recently attracted gr…