1 citations · 1 across the 8 of their papers we have counts for
8 papers
Video-KTR: Reinforcing Video Reasoning via Key Token Attribution
Ziyue Wang, Sheng Jin, Zhongrong Zuo +5
Reinforcement learning (RL) has shown strong potential for enhancing reasoning in multimodal large language models, yet existing video reasoning methods often rely on coarse sequen…
A Judge-Aware Ranking Framework for Evaluating Large Language Models without Ground Truth
Mingyuan Xu, Xinzi Tan, Jiawei Wu +1
Evaluating large language models (LLMs) on open-ended tasks without ground-truth labels is increasingly done via the LLM-as-a-judge paradigm. A critical but under-modeled issue is…
veScale: Consistent and Efficient Tensor Programming with Eager-Mode SPMD
Youjie Li, Cheng Wan, Zhiqi Lin +10
Large Language Models (LLMs) have scaled rapidly in size and complexity, requiring increasingly intricate parallelism for distributed training, such as 3D parallelism. This sophist…
3D Question Answering via only 2D Vision-Language Models
Fengyun Wang, Sicheng Yu, Jiawei Wu +3
Large vision-language models (LVLMs) have significantly advanced numerous fields. In this work, we explore how to harness their potential to address 3D scene understanding tasks, u…
Sparse-to-Dense: A Free Lunch for Lossless Acceleration of Video Understanding in LLMs
Xuan Zhang, Cunxiao Du, Sicheng Yu +4
Due to the auto-regressive nature of current video large language models (Video-LLMs), the inference latency increases as the input sequence length grows, posing challenges for the…
Reverse Modeling in Large Language Models
Sicheng Yu, Yuanchen Xu, Cunxiao Du +5
Humans are accustomed to reading and writing in a forward manner, and this natural bias extends to text understanding in auto-regressive large language models (LLMs). This paper in…
Frame-Voyager: Learning to Query Frames for Video Large Language Models
Sicheng Yu, Chengkai Jin, Huanyu Wang +9
Video Large Language Models (Video-LLMs) have made remarkable progress in video understanding tasks. However, they are constrained by the maximum length of input tokens, making it…
GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding
Cunxiao Du, Jing Jiang, Xu Yuanchen +8
Speculative decoding is a relatively new decoding framework that leverages small and efficient draft models to reduce the latency of LLMs. In this study, we introduce GliDe and CaP…