5 papers
Bridging Coarse and Fine Recognition: A Hybrid Approach for Open-Ended Multi-Granularity Object Recognition in Interactive Educational Games
Hanling Yi, Feng Lin, Mao Luo +3
Recent advances in Multimodal Large Language Models (MLLMs) have enabled open-ended object recognition, yet they struggle with fine-grained tasks. In contrast, CLIP-style models ex…
HCAttention: Extreme KV Cache Compression via Heterogeneous Attention Computing for LLMs
Dongquan Yang, Yifan Yang, Xiaotian Yu +2
Processing long-context inputs with large language models presents a significant challenge due to the enormous memory requirements of the Key-Value (KV) cache during inference. Exi…
Not All Attention Heads Are What You Need: Refining CLIP's Image Representation with Attention Ablation
Feng Lin, Marco Chen, Haokui Zhang +3
This paper investigates the role of attention heads in CLIP's image encoder. Building on interpretability studies, we conduct an exhaustive analysis and find that certain heads, di…
Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding
Hanling Yi, Feng Lin, Hongbin Li +3
This research aims to accelerate the inference speed of large language models (LLMs) with billions of parameters. We propose \textbf{S}mart \textbf{P}arallel \textbf{A}uto-\textbf{…
BiTA: Bi-Directional Tuning for Lossless Acceleration in Large Language Models
Feng Lin, Hanling Yi, Hongbin Li +4
Large language models (LLMs) commonly employ autoregressive generation during inference, leading to high memory bandwidth demand and consequently extended latency. To mitigate this…