2 citations · 2 across the 8 of their papers we have counts for
8 papers
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
Q Cache: Visual Attention is Valuable in Less than Half of Decode Layers for Multimodal Large Language Model
Jiedong Zhuang, Lu Lu, Ming Dai +4
Multimodal large language models (MLLMs) are plagued by exorbitant inference costs attributable to the profusion of visual tokens within the vision encoder. The redundant visual to…
Revisiting Cross-Architecture Distillation: Adaptive Dual-Teacher Transfer for Lightweight Video Models
Ying Peng, Hongsen Ye, Changxin Huang +3
Vision Transformers (ViTs) have achieved strong performance in video action recognition, but their high computational cost limits their practicality. Lightweight CNNs are more effi…
GPT-5 Model Corrected GPT-4V's Chart Reading Errors, Not Prompting
Kaichun Yang, Jian Chen
We present a quantitative evaluation to understand the effect of zero-shot large-language model (LLMs) and prompting uses on chart reading tasks. We asked LLMs to answer 107 visual…
KnowMT-Bench: Benchmarking Knowledge-Intensive Long-Form Question Answering in Multi-Turn Dialogues
Junhao Chen, Yu Huang, Siyuan Li +7
Multi-Turn Long-Form Question Answering (MT-LFQA) is a key application paradigm of Large Language Models (LLMs) in knowledge-intensive domains. However, existing benchmarks are lim…
Rectified Sparse Attention
Yutao Sun, Tianzhu Ye, Li Dong +6
Efficient long-sequence generation is a critical challenge for Large Language Models. While recent sparse decoding methods improve efficiency, they suffer from KV cache misalignmen…