Papers by Guoliang He
A Novel Matching Paradigm: Unified Generative and Discriminative LLM with Prompt Compression for Relevance Learning (2026.acl-industry)
Copied to clipboard
Guoliang Zhao, Zixin Cui, Chao Ye, Dengwu He, Fei Huang, Yubo Liu, Shuanglong Li, Tzungren Kuo, Bin Ding, Shuang Zhang, null KunhongZhu, Zhi Guo, Liu Lin
| Challenge: | Existing approaches to matching use Large Language Models as feature extractors, underutilizing their full modeling capabilities. |
| Approach: | They propose a matching paradigm that integrates two-tower, single-towing, and generative tasks within a unified LLM framework via attention-mask partitioning. |
| Outcome: | The proposed model achieves superior performance and strong practical value in an industrial search engine. |
PromptKeeper: Safeguarding System Prompts for LLMs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | PromptKeeper is a defense mechanism designed to safeguard system prompts . adversarial and regular queries can exploit LLM vulnerabilities to expose hidden prompts. |
| Approach: | PromptKeeper is a defense mechanism designed to safeguard system prompts . it detects both explicit and subtle leakage and regenerates responses using a dummy prompt . |
| Outcome: | PromptKeeper detects and mitigates side-channel vulnerabilities when prompts are exposed . it regenerates responses using a dummy prompt, ensuring outputs remain indistinguishable from typical interactions . |