12 citations · 21 across the 15 of their papers we have counts for
15 papers
CMT: A Memory Compression Method for Continual Knowledge Learning of Large Language Models
Dongfang Li, Zetian Sun, Xinshuo Hu +2
Large Language Models (LLMs) need to adapt to the continuous changes in data, tasks, and user preferences. Due to their massive size and the high costs associated with training, LL…
RaSeRec: Retrieval-Augmented Sequential Recommendation
Xinping Zhao, Baotian Hu, Yan Zhong +5
Although prevailing supervised and self-supervised learning augmented sequential recommendation (SeRec) models have achieved improved performance with powerful neural network archi…
Anim-Director: A Large Multimodal Model Powered Agent for Controllable Animation Video Generation
Yunxin Li, Haoyuan Shi, Baotian Hu +5
Traditional animation generation methods depend on training generative models with human-labelled data, entailing a sophisticated multi-stage pipeline that demands substantial huma…
VideoVista: A Versatile Benchmark for Video Understanding and Reasoning
Yunxin Li, Xinyu Chen, Baotian Hu +3
Despite significant breakthroughs in video analysis driven by the rapid development of large multimodal models (LMMs), there remains a lack of a versatile evaluation benchmark to c…
Improving Attributed Text Generation of Large Language Models via Preference Learning
Dongfang Li, Zetian Sun, Baotian Hu +4
Large language models have been widely adopted in natural language processing, yet they face the challenge of generating unreliable content. Recent works aim to reduce misinformati…
A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation
Yunxin Li, Baotian Hu, Wenhan Luo +3
In this paper, we propose a new setting for generating product descriptions from images, augmented by marketing keywords. It leverages the combined power of visual and textual info…