306 citations · 306 across the 5 of their papers we have counts for
5 papers
OmniSelect: Dynamic Modality-Aware Token Compression for Efficient Omni-modal Large Language Models
Morunliu Yang, Ruotao Xu, Le Li +6
Omnimodal large language models (OmniLLMs) have recently gained increasing attention for unified audio-video understanding. However, processing long multimodal token sequences intr…
CoCoReviewBench: A Completeness- and Correctness-Oriented Benchmark for AI Reviewers
Hexuan Deng, Xiaopeng Ke, Yichen Li +6
Despite the rapid development of AI reviewers, evaluating such systems remains challenging: metrics favor overlap with human reviews over correctness. However, since human reviews…
G-SPEED: General SParse Efficient Editing MoDel
Haoke Zhang, Yue Wang, Juntao Li +2
Large Language Models~(LLMs) have demonstrated incredible capabilities in understanding, generating, and manipulating languages. Through human-model interactions, LLMs can automati…
Harnessing the Power of David against Goliath: Exploring Instruction Data Generation without Using Closed-Source Models
Yue Wang, Xinrui Wang, Juntao Li +5
Instruction tuning is instrumental in enabling Large Language Models~(LLMs) to follow user instructions to complete various open-domain tasks. The success of instruction tuning dep…
R-Drop: Regularized Dropout for Neural Networks
Xiaobo Liang, Lijun Wu, Juntao Li +6
Dropout is a powerful and widely used technique to regularize the training of deep neural networks. In this paper, we introduce a simple regularization strategy upon dropout in mod…