1 citations · 1 across the 5 of their papers we have counts for
5 papers
LADR: Locality-Aware Dynamic Rescue for Efficient Text-to-Image Generation with Diffusion Large Language Models
Chenglin Wang, Yucheng Zhou, Shawn Chen +2
Discrete Diffusion Language Models have emerged as a compelling paradigm for unified multimodal generation, yet their deployment is hindered by high inference latency arising from…
Reinforcement Learning with Promising Tokens for Large Language Models
Jing-Cheng Pang, Liang Lu, Xian Tang +4
Reinforcement learning (RL) has emerged as a key paradigm for aligning and optimizing large language models (LLMs). Standard approaches treat the LLM as the policy and apply RL dir…
EDCO: Dynamic Curriculum Orchestration for Domain-specific Large Language Model Fine-tuning
Jing-Cheng Pang, Liu Sun, Chang Zhou +10
Domain-specific large language models (LLMs), typically developed by fine-tuning a pre-trained general-purpose LLM on specialized datasets, represent a significant advancement in a…
LFS: Learnable Frame Selector for Event-Aware and Temporally Diverse Video Captioning
Lianying Chao, Linfeng Yin, Peiyu Ren +8
Video captioning models convert frames into visual tokens and generate descriptions with large language models (LLMs). Since encoding all frames is prohibitively expensive, uniform…
Multi-Modal LLM based Image Captioning in ICT: Bridging the Gap Between General and Industry Domain
Lianying Chao, Kai Zhang, Haoran Cai +3
In the information and communications technology (ICT) industry, training a domain-specific large language model (LLM) or constructing a retrieval-augmented generation system requi…