Papers by Yanbin Huang
MMDEND: Dendrite-Inspired Multi-Branch Multi-Compartment Parallel Spiking Neuron for Sequence Modeling (2025.acl-long)
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Kexin Wang, Yuhong Chou, Di Shang, Shijie Mei, Jiahong Zhang, Yanbin Huang, Man Yao, Bo Xu, Guoqi Li
| Challenge: | Vanilla spiking neurons are simplified from complex biological neurons with dendrites, soma, and synapses into single somatic compartments. |
| Approach: | They propose a multi-branch, multi-compartment parallel spiking dendritic neuron with a proportion-adjustable multi-branched structure that enables long-term temporal dependencies. |
| Outcome: | The proposed model achieves better long-sequence modeling capability with fewer parameters and lower energy consumption. |
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion (2023.findings-emnlp)
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| Challenge: | Existing knowledge graph completion methods struggle with long-tail entities due to limited structural information and imbalanced distributions of entities. |
| Approach: | They propose a framework that integrates a large language model and a triple-based KGC retriever to alleviate the long-tail problem without incurring additional training overhead. |
| Outcome: | The proposed model reduces training overhead and finetuning costs on benchmark datasets. |
RecMind: Large Language Model Powered Agent For Recommendation (2024.findings-naacl)
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Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Yanbin Lu, Xiaojiang Huang, Yingzhen Yang
| Challenge: | Existing recommendations systems are limited in generalizing to new tasks due to model scale and data size constraints. |
| Approach: | They propose an LLM-powered autonomous recommender agent, RecMind, which is capable of leveraging external knowledge to provide zero-shot personalized recommendations. |
| Outcome: | The proposed model outperforms existing zero/few-shot LLM-based recommendation baseline methods in various tasks and achieves comparable performance to a fully trained recommendation model P5. |
Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding (2023.emnlp-industry)
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| Challenge: | Defective queries impact the robustness of conversational AI systems such as Alexa, Siri or Google Assistant. |
| Approach: | They propose a Personalized Query Rewriting system that takes into account individual preferences or unique error patterns identified from a user's historical interactions with the conversational AI. |
| Outcome: | The proposed approach has been proven on a large-scale real-world dataset and online A/B experiments. |