Papers by Xinghua Zhang
How Alignment and Jailbreak Work: Explain LLM Safety through Intermediate Hidden States (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) rely on safety alignment to avoid malicious user inputs. |
| Approach: | They employ weak classifiers to explain LLM safety through the intermediate hidden states. |
| Outcome: | The proposed model can identify malicious and normal inputs and detect malicious ones without jailbreak. |
IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimization (2025.acl-long)
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| Challenge: | Existing algorithms to improve the ability of LLMs to follow complex instructions are lacking. |
| Approach: | They propose a benchmark to improve the ability to follow complex instructions by using a IOPO alignment method to take input and output preference into consideration. |
| Outcome: | The proposed algorithm shows 8.15%, 2.18% improvements on in-domain data and 5.91%, 2.83% on out-of-domain datasets compared to SFT and DPO respectively. |
LearnAlign: Data Selection for LLM Reinforcement Learning with Improved Gradient Alignment (2026.findings-acl)
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Shipeng Li, Zhiqin Yang, Shikun Li, Xiaobo Xia, Hengyu Liu, Xinghua Zhang, Gaode Chen, Dong Fang, Ying Tai, Zhe Peng
| Challenge: | Reinforcement learning with verifiable rewards (RLVR) is a key technique for enhancing LLMs’ reasoning abilities, yet its data inefficiency remains a major bottleneck. |
| Approach: | They propose a gradient-alignment-based method which intelligently selects the learnable and representative training reasoning data for RLVR post-training. |
| Outcome: | Experiments on five reasoning benchmarks show that the proposed method significantly reduces training data requirements while improving performance. |
Revealing and Mitigating the Challenge of Detecting Character Knowledge Errors in LLM Role-Playing (2025.emnlp-main)
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| Challenge: | Existing studies on large language models (LLMs) fail to detect character knowledge errors, leading to low-quality automatic corpus construction. |
| Approach: | They propose to use a large language model to detect known knowledge errors and an agent-based reasoning method to improve error detection. |
| Outcome: | The proposed method improves the ability of LLMs to detect errors in known knowledge errors and unknown knowledge errors while playing roles. |
ExpSeek: Self-Triggered Experience Seeking for Web Agents (2026.findings-acl)
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Wenyuan Zhang, Xinghua Zhang, Haiyang Yu, Shuaiyi Nie, Bingli Wu, Juwei Yue, Tingwen Liu, Yongbin Li
| Challenge: | Existing methods for integrating experience into web agents are struggling to adapt to dynamically changing contextual observations during agent-environment interaction. |
| Approach: | They propose a model that shifts experience toward step-level proactive seeking by estimating step- level entropy thresholds and designing step-Level tailored experience content. |
| Outcome: | The proposed model achieves 9.3% and 7.5% performance improvements on Qwen3-8B and 32B models across four challenging web agent benchmarks. |
DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling (2025.findings-acl)
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| Challenge: | Large language models (LLMs) enabled dialogue systems are one of the central modes in human-machine interaction. |
| Approach: | They propose a benchmark task for dialogue element MOdeling and Element Awareness and a new benchmark for dialogue agent interaction that allows the agent to model dialogue elements via imitation learning. |
| Outcome: | The proposed agent performs well in both dialogue element modeling and out-of-domain tasks. |
Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning (2021.findings-emnlp)
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| Challenge: | Named entity recognition (NER) is a method of detecting entity spans and classifying them into predefined categories. |
| Approach: | They propose a method to iteratively perform noisy label refinery by using self-collaborative denoising learning. |
| Outcome: | The proposed learning paradigm exploits reliable labels and communicates with unreliable annotations by collaborative denoising. |
EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language Models (2025.emnlp-main)
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| Challenge: | Existing benchmarks focusing on single-task environments with limited constraints lack the complexity required to fully reflect the evolution of large language models (LLMs). |
| Approach: | They propose to use a Segment Policy Optimization algorithm to enhance the LLM's ability to accurately fulfill multi-task workflows. |
| Outcome: | The proposed benchmarks show that existing benchmarks lack the complexity required to fully reflect the evolution of large language models. |
Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)
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Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li
| Challenge: | Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications. |
| Approach: | They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) . |
| Outcome: | The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents. |
Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning (2021.emnlp-main)
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| Challenge: | Named entity recognition (NER) is a method of detecting entity spans and classifying them into predefined categories. |
| Approach: | They propose a method to iteratively perform noisy label refinery by using self-collaborative denoising learning. |
| Outcome: | The proposed learning paradigm exploits reliable labels and communicates with unreliable annotations by collaborative denoising. |
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. |