1 citations · 1 across the 6 of their papers we have counts for
6 papers
Multimodal Representation-disentangled Information Bottleneck for Multimodal Recommendation
Hui Wang, Jinghui Qin, Wushao Wen +3
Multimodal data has significantly advanced recommendation systems by integrating diverse information sources to model user preferences and item characteristics. However, these syst…
AssoCiAm: A Benchmark for Evaluating Association Thinking while Circumventing Ambiguity
Yifan Liu, Wenkuan Zhao, Shanshan Zhong +4
Recent advancements in multimodal large language models (MLLMs) have garnered significant attention, offering a promising pathway toward artificial general intelligence (AGI). Amon…
Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction
Qingling Li, Wushao Wen, Jinghui Qin
The Aspect Sentiment Triplet Extraction (ASTE) task aims to extract aspect terms, opinion terms, and their corresponding sentiment polarity from a given sentence. It remains one of…
MoExtend: Tuning New Experts for Modality and Task Extension
Shanshan Zhong, Shanghua Gao, Zhongzhan Huang +3
Large language models (LLMs) excel in various tasks but are primarily trained on text data, limiting their application scope. Expanding LLM capabilities to include vision-language…
Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima
Shanshan Zhong, Zhongzhan Huang, Daifeng Li +3
Multimodal recommender systems utilize various types of information to model user preferences and item features, helping users discover items aligned with their interests. The inte…
Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation
Shanshan Zhong, Zhongzhan Huang, Shanghua Gao +4
Chain-of-Thought (CoT) guides large language models (LLMs) to reason step-by-step, and can motivate their logical reasoning ability. While effective for logical tasks, CoT is not c…