5 papers
MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models
Kailin Jiang, Ning Jiang, Yuntao Du +8
Large Multimodal Models (LMMs) encode rich factual knowledge via cross-modal pre-training, yet their static representations struggle to maintain an accurate understanding of time-s…
Massive Memorization with Hundreds of Trillions of Parameters for Sequential Transducer Generative Recommenders
Zhimin Chen, Chenyu Zhao, Ka Chun Mo +7
Modern large-scale recommendation systems rely heavily on user interaction history sequences to enhance the model performance. The advent of large language models and sequential mo…
A Systematic Evaluation of On-Device LLMs: Quantization, Performance, and Resources
Qingyu Song, Rui Liu, Wei Lin +11
Deploying Large Language Models (LLMs) on edge devices enhances privacy but faces performance hurdles due to limited resources. We introduce a systematic methodology to evaluate on…
When Large Multimodal Models Confront Evolving Knowledge: Challenges and Explorations
Kailin Jiang, Yuntao Du, Yukai Ding +7
Large Multimodal Models (LMMs) store vast amounts of pretrained knowledge but struggle to remain aligned with real-world updates, making it difficult to avoid capability degradatio…
Portal UX Agent -- A Plug-and-Play Engine for Rendering UIs from Natural Language Specifications
Xinsong Li, Ning Jiang, Jay Selvaraj
The rapid appearance of large language models (LLMs) has led to systems that turn natural-language intent into real user interfaces (UIs). Free-form code generation maximizes expre…