Papers by Haiyang Guo
HiDe-LLaVA: Hierarchical Decoupling for Continual Instruction Tuning of Multimodal Large Language Model (2025.acl-long)
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| Challenge: | Existing methods to improve instructionfollowing performance of MLLMs often trade off memory efficiency for performance gains, compromising overall efficiency. |
| Approach: | They propose a task-specific expansion and task-general fusion framework based on variations in Centered Kernel Alignment (CKA) similarity across different model layers when trained on diverse datasets. |
| Outcome: | The proposed framework improves performance compared to existing benchmarks. |
Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacement (2025.emnlp-industry)
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Haotan Guo, Jianfei He, Jiayuan Ma, Hongbin Na, Zimu Wang, Haiyang Zhang, Qi Chen, Wei Wang, Zijing Shi, Tao Shen, Ling Chen
| Challenge: | Phonetic Cloaking Replacement (PCR) is a problem in content moderation in China. |
| Approach: | They organize PCR into a four-way surface-form taxonomy and compile PCR-ToxiCN, a dataset of 500 phonetically cloaked offensive posts gathered from the RedNote platform. |
| Outcome: | The proposed model achieves only an F1-score and zero-shot chain-of-thought prompting pushes performance even lower. |
FAQ: Mitigating Quantization Error via Regenerating Calibration Data with Family-Aware Quantization (2026.findings-acl)
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| Challenge: | representativeness and universality of calibration data remain a bottleneck in quantization accuracy. |
| Approach: | They propose a framework that leverages prior knowledge from LLMs to generate calibration samples . their framework reduces accuracy loss by up to 28.5% compared to baseline . |
| Outcome: | Experiments show that family-aware quantization reduces accuracy loss by up to 28.5% compared to baseline data. |
ModalPrompt: Towards Efficient Multimodal Continual Instruction Tuning with Dual-Modality Guided Prompt (2025.emnlp-main)
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| Challenge: | Existing MCIT methods do not fully exploit the unique attribute of Large Multimodal Models and often gain performance at the expense of efficiency. |
| Approach: | They propose a multimodal continual instruction learning framework that exploits the ability of LMMs to learn mixed instruction datasets and prompts for each task. |
| Outcome: | The proposed framework achieves +14.26% performance gain on MCIT benchmarks with remarkable x1.42 inference speed free from growing computation. |
MemPO: Self-Memory Policy Optimization for Long-Horizon Agents (2026.findings-acl)
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Ruoran Li, Xinghua Zhang, Haiyang Yu, Shitong Duan, Xiang Li, Wenxin Xiang, Chonghua Liao, Xudong Guo, Yongbin Li, Jinli Suo
| Challenge: | Existing methods for long-horizon agents introduce the external memory module and look up the relevant information from the stored memory, which prevents the model from proactively managing its memory content and aligning with the agent’s overarching task objectives. |
| Approach: | They propose an algorithm which enables agents to autonomously manage their memory during interaction with environment and selectively retain crucial information. |
| Outcome: | Extensive experiments show that the proposed algorithm achieves absolute F1 score gains of 25.98 over the base model and 7.1 over the previous SOTA baseline while preserving task performance. |