Papers by Shuyang Hao
Adaptive Prompt Optimization for Open-Ended Tasks: Uncertainty Preference as a Secondary Signal (2026.findings-acl)
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| Challenge: | Recent training-free prompt optimizers treat performance as maximizing a single scalar score and ignore a second signal that the desired style is task dependent. |
| Approach: | They propose a semantic-entropy-based method that uses task uncertainty to guide prompt optimization by selecting high-entropicy candidates for creative tasks and low-energetic candidates for conservative ones. |
| Outcome: | The proposed method outperforms baselines on MT-Bench subsets and integrates easily into existing prompt optimizers. |
EpiGEN: An Efficient Multi-Api Code GENeration Framework under Enterprise Scenario (2024.lrec-main)
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| Challenge: | Existing approaches to large language models fail to meet expectations for code generation tasks . existing approaches are faced with drawbacks of high resource consumption and inadequate handling of multi-API tasks. |
| Approach: | They propose an Efficient multi-Api code GENeration framework that uses private APIs to pre-train LLMs. |
| Outcome: | The proposed framework shows good acceptability and readability on single-GPU tasks compared to fully fine-tuned LLMs with a larger number of parameters. |
HyperAdaLoRA: Accelerating LoRA Rank Allocation During Training via Hypernetworks without Sacrificing Performance (2026.findings-acl)
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Hao Zhang, Zhenjia Li, Yifan Gao, Xi Xiao, Heng Zhang, Shuyang Zhang, null Xiaoxincc, Bo Huang, Yuhang Wu, Tianyang Wang, Hao Xu
| Challenge: | Low-Rank Adaptation (LoRA) assumes a uniform rank r for each incremental matrix, not accounting for the varying significance of weight matrices across modules and layers. |
| Approach: | They propose a framework that allows for faster convergence of low-rank adaptive models . they use a hypernetwork to prune the outputs of the hypernetworks to generate parameters . |
| Outcome: | The proposed framework accelerates convergence of AdaLoRA by leveraging a hypernetwork. |
Making Every Step Effective: Jailbreaking Large Vision-Language Models Through Hierarchical KV Equalization (2025.findings-emnlp)
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| Challenge: | HKVE selectively accepts gradient optimization results based on the distribution of attention scores across different layers, ensuring that every optimization step positively contributes to the attack. |
| Approach: | They propose a framework that selectively accepts gradient optimization results based on the distribution of attention scores across different layers and selectively takes them into account when calculating the attack success rate. |
| Outcome: | The proposed framework outperforms existing methods by achieving success rates of 75.08% on MiniGPT4, 85.84% on LLaVA and 81.00% on Qwen-VL. |