Papers by Guangyan Zhang
MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper (2025.emnlp-main)
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Runjia Zeng, Guangyan Sun, Qifan Wang, Tong Geng, Sohail Dianat, Xiaotian Han, Raghuveer Rao, Xueling Zhang, Cheng Han, Lifu Huang, Dongfang Liu
| Challenge: | Empirical evaluations show that Mixture of Expert Prompt Tuning outperforms state-of-the-art parameter efficient baselines on SuperGLUE. |
| Approach: | They propose a pretrain-then-fine-tune paradigm for manifold mapping using multiple prompt experts. |
| Outcome: | Empirical results show that the proposed approach outperforms state-of-the-art methods on SuperGLUE while reducing activated prompts by 79.25%. |
Recent Advances in Speech Language Models: A Survey (2025.acl-long)
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Wenqian Cui, Dianzhi Yu, Xiaoqi Jiao, Ziqiao Meng, Guangyan Zhang, Qichao Wang, Steven Y. Guo, Irwin King
| Challenge: | Text-based Large Language Models (LLMs) are a promising solution for end-to-end speech interaction. |
| Approach: | They propose to build a framework that allows users to input text and translate it into speech . they propose to use a text-only LLM and a "textto-speech" framework to generate a response based on this transcription . |
| Outcome: | The survey offers an overview of recent approaches to building SpeechLMs . it outlines core architectural components, training methodologies, evaluation strategies and challenges . |
Dual Activation-Weight Sparsity: A Training-Free Framework for Efficient Large Language Model Compression (2026.acl-long)
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Luoyang Sun, Guangyan Li, Cheng Deng, Haifeng Zhang, Jian Zhao, Yongqiang Tang, Wensheng Zhang, Jun Wang
| Challenge: | Large language models (LLMs) excel at natural language tasks but face deployment bottlenecks due to computational demands. |
| Approach: | They propose a training-free framework that exploits activation and weight sparsity . they use a three-tier routing strategy that uses magnitude-based pruning . |
| Outcome: | Experiments on Llama and Mistral models show that DAWS outperforms activation-weight sparsity pruning methods. |