Papers by Haodong Zou
Rejection-to-Acceptance Transition: Model Editing-Based Jailbreak Backdoor Injection Not Limited to Few Output Tokens (2026.findings-acl)
Copied to clipboard
Shiji Yang, Min Cai, Hao Xiong, Congyao Mei, Haodong Zou, Shicheng Tan, Jie Chen, Fulan Qian, Shu Zhao
| Challenge: | Existing methods for jailbreaking LLMs are implemented by binding backdoors to predefined phrases as first few output tokens, inducing the LLM’s next-token prediction to produce continuous responses. |
| Approach: | They propose a model editing-based jailbreak backdoor attack that hijacks LLM representations into a acceptance domain rather than binding to a few output tokens. |
| Outcome: | The proposed model editing method outperforms existing methods, showing stronger jailbreak capabilities across LLMs and datasets. |
DUD: Decoupled Update Dynamics for Reliable Uncertainty Quantification in Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs). |
| Approach: | They propose a framework that explicitly decouples FFN and Attention contributions via noise-induced causal interventions to capture model's internal fragility. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in both uncertainty estimation and calibration while exhibiting superior cross-dataset generalization. |
Execution as Verification: Fine-Grained Self-Correcting Reasoning for Complex KBQA (2026.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge base question answering methods are limited by syntactic constraints and are prone to structural deviations that render queries unexecutable. |
| Approach: | They propose a framework that reframes semantic parsing as an iterative reasoning process driven by execution feedback. |
| Outcome: | The proposed method achieves significant improvements in query executability and answer accuracy on the WebQSP and CWQ datasets. |
CompTab: A Comprehensive Benchmark for Real-World TableQA with Complex Reasoning and Irregular Tables (2026.acl-long)
Copied to clipboard
Zhen Yang, Wei Du, Jie Wang, Wenze Zhou, Xiangfeng Meng, Zhengyang Wang, Suping Sun, Ziwei Du, Haodong Zou, Jie Chen, Yongbin Liu, Shicheng Tan, Jiahao Ying, Shu Zhao
| Challenge: | Existing benchmarks focus on well-structured tables and fail to reflect irregular structures and complex reasoning commonly encountered in real-world scenarios. |
| Approach: | They propose a benchmark to evaluate TableQA under complex reasoning and irregular table conditions. |
| Outcome: | The proposed framework improves generalization and realism of large language models under complex and irregular table conditions. |
Thinking Beyond the Local: Multi-View Instructed Adaptive Reasoning in KG-Enhanced LLMs (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods for large language models adopt query-driven iterative reasoning from a local perspective, limiting efficiency and accuracy for complex multi-hop tasks. |
| Approach: | They propose a multi-view instructed adaptive reasoning of LLM on Knowledge Graphs that allows LLMs to plan, evaluate, and adapt reasoning paths from a global perspective. |
| Outcome: | The proposed model overcomes the limitations of local exploration by enabling LLMs to plan, evaluate, and adapt reasoning paths from a global perspective. |