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Your search keyword '"Zhong, Yangyang"' showing total 3 results
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3 results on '"Zhong, Yangyang"'

1. Unveiling the Impact of Multi-Modal Interactions on User Engagement: A Comprehensive Evaluation in AI-driven Conversations

Author

Zhang, Lichao, Yu, Jia, Zhang, Shuai, Li, Long, Zhong, Yangyang, Liang, Guanbao, Yan, Yuming, Ma, Qing, Weng, Fangsheng, Pan, Fayu, Li, Jing, Xu, Renjun, and Lan, Zhenzhong

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Large Language Models (LLMs) have significantly advanced user-bot interactions, enabling more complex and coherent dialogues. However, the prevalent text-only modality might not fully exploit the potential for effective user engagement. This paper explores the impact of multi-modal interactions, which incorporate images and audio alongside text, on user engagement in chatbot conversations. We conduct a comprehensive analysis using a diverse set of chatbots and real-user interaction data, employing metrics such as retention rate and conversation length to evaluate user engagement. Our findings reveal a significant enhancement in user engagement with multi-modal interactions compared to text-only dialogues. Notably, the incorporation of a third modality significantly amplifies engagement beyond the benefits observed with just two modalities. These results suggest that multi-modal interactions optimize cognitive processing and facilitate richer information comprehension. This study underscores the importance of multi-modality in chatbot design, offering valuable insights for creating more engaging and immersive AI communication experiences and informing the broader AI community about the benefits of multi-modal interactions in enhancing user engagement.

Published
2024

3. ADIR: Advanced domain‐invariant representation via decoupling learning and information bottleneck.

Author

Zhong, Yangyang, Yan, Yunfeng, Luo, Pengxin, Zhou, Yuhao, and Qi, Donglian

Subjects
*CONVOLUTIONAL neural networks, *MATHEMATICAL decoupling, *IMAGE reconstruction, *DATA distribution
Abstract

The discrepancy in data distribution between training and testing scenarios, as well as the inductive bias of convolutional neural networks towards image styles, reduces the model's generalization ability. Many unsupervised domain generalization methods based on feature decoupling suffer from an initial neglect of explicit decoupling of content and style features, resulting in content features that still contain considerable redundant information, thereby restricting improvements in generalization capability. To tackle this problem, this paper optimizes the learning process of domain‐invariant (content) features into an information compression issue, minimizing redundancy in content features. Furthermore, to enhance decoupled learning, this paper introduces innovative cross‐domain loss functions and image reconstruction modules that explicitly decouple and merge content and style across different domains. Extensive experiments demonstrate the method's significant enhancements over recent cutting‐edge approaches. [ABSTRACT FROM AUTHOR]

Published
2024
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