7 papers
SkillSight: Calibrating Generic Content Bias for Skill Retrieval
Jinying Xiao, Bin Li, Bin Ji +9
As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable capability selection and execution. Existi…
Outlier Smoothing with Closed-Form Rotations for W4A4 Large Language Model Quantization
Jinying Xiao, Bin Ji, Shasha Li +8
Large Language Models (LLMs) quantization facilitates deploying LLMs in resource-limited settings, but existing methods that combine incompatible gradient optimization and quantiza…
EMSEdit: Efficient Multi-Step Meta-Learning-based Model Editing
Xiaopeng Li, Shasha Li, Xi Wang +7
Large Language Models (LLMs) power numerous AI applications, yet updating their knowledge remains costly. Model editing provides a lightweight alternative through targeted paramete…
Camel: Energy-Aware LLM Inference on Resource-Constrained Devices
Hao Xu, Long Peng, Shezheng Song +5
Most Large Language Models (LLMs) are currently deployed in the cloud, with users relying on internet connectivity for access. However, this paradigm faces challenges such as netwo…
LSAQ: Layer-Specific Adaptive Quantization for Large Language Model Deployment
Binrui Zeng, Bin Ji, Xiaodong Liu +7
As Large Language Models (LLMs) demonstrate exceptional performance across various domains, deploying LLMs on edge devices has emerged as a new trend. Quantization techniques, whic…
How to Alleviate Catastrophic Forgetting in LLMs Finetuning? Hierarchical Layer-Wise and Element-Wise Regularization
Shezheng Song, Hao Xu, Jun Ma +5
Large Language Models (LLMs) exhibit strong general language capabilities. However, fine-tuning these models on domain-specific tasks often leads to catastrophic forgetting, where…