12 papers
Long Exposure: Accelerating Parameter-Efficient Fine-Tuning for LLMs under Shadowy Sparsity
Tuowei Wang, Kun Li, Zixu Hao +5
The adaptation of pre-trained large language models (LLMs) to diverse downstream tasks via fine-tuning is critical for numerous applications. However, the inefficiency of parameter…
Neuralink: Fast LLM Inference on Smartphones with Neuron Co-Activation Linking
Tuowei Wang, Ruwen Fan, Minxing Huang +6
Large Language Models (LLMs) have achieved remarkable success across various domains, yet deploying them on mobile devices remains an arduous challenge due to their extensive compu…
ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning
Ziqing Qiao, Yongheng Deng, Jiali Zeng +7
Large Reasoning Models (LRMs) perform strongly in complex reasoning tasks via Chain-of-Thought (CoT) prompting, but often suffer from verbose outputs, increasing computational over…
LLM-Driven Self-Refinement for Embodied Drone Task Planning
Deyu Zhang, Xicheng Zhang, Jiahao Li +6
We introduce SRDrone, a novel system designed for self-refinement task planning in industrial-grade embodied drones. SRDrone incorporates two key technical contributions: First, it…
Prompt-aware of Frame Sampling for Efficient Text-Video Retrieval
Deyu Zhang, Tingting Long, Jinrui Zhang +3
Enabling efficient text-video retrieval on edge-end devices is critical for real-world applications. Yet, existing methods face a critical challenge in balancing accuracy and compu…
AugFL: Augmenting Federated Learning with Pretrained Models
Sheng Yue, Zerui Qin, Yongheng Deng +3
Federated Learning (FL) has garnered widespread interest in recent years. However, owing to strict privacy policies or limited storage capacities of training participants such as I…