Papers by Yuhong Zhang
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. |
Towards Provably Secure Generative AI: Reliable Consensus Sampling (2026.findings-acl)
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Yu Cui, Hang Fu, Sicheng Pan, Zhuoyu Sun, Yifei Liu, Yuhong Nie, Bo Ran, Baohan Huang, Xufeng Zhang, Haibin Zhang, Cong Zuo, Licheng Wang
| Challenge: | Existing research on generative AI security is driven by mutually reinforcing attack and defense methodologies grounded in empirical experience. |
| Approach: | They propose a new algorithm that uses a random sampling algorithm to control risk. |
| Outcome: | The proposed algorithm improves robustness and utility while maintaining latency comparable to existing algorithms. |
Pretraining Context Compressor for Large Language Models with Embedding-Based Memory (2025.acl-long)
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| Challenge: | Efficient processing of long contexts in large language models is essential for real-world applications such as retrieval-augmented generation and in-context learning. |
| Approach: | They propose a decoupled compressor-LLM framework that preserves contextual information within condensed embedding representations. |
| Outcome: | The proposed framework outperforms baseline models in three domains and across eight datasets while adapting to different downstream LLMs. |
BP4ER: Bootstrap Prompting for Explicit Reasoning in Medical Dialogue Generation (2024.lrec-main)
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| Challenge: | Medical dialogue generation (MDG) has gained increasing attention due to its substantial practical value. |
| Approach: | They propose a method which explicitly models MDG’s multi-step reasoning process and iteratively enhances this reasoning process. |
| Outcome: | The proposed method outperforms state-of-the-art methods across objective and subjective evaluations on two publicly available datasets. |
Learning Inter-Entity-Interaction for Few-Shot Knowledge Graph Completion (2022.emnlp-main)
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| Challenge: | Recent FKGC studies focus on learning semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities. |
| Approach: | They propose a model to learn semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two public datasets. |