Papers by Xianren Zhang
Divide-Verify-Refine: Can LLMs Self-align with Complex Instructions? (2025.findings-acl)
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| Challenge: | Existing research shows LLMs struggle with complex instructions involving multiple constraints. |
| Approach: | They propose a framework to divide complex instructions into single constraints and prepare appropriate tools to verify responses. |
| Outcome: | The proposed framework doubles Llama3.1-8B’s constraint adherence and triples Mistral-7B’ s performance. |
Image Corruption-Inspired Membership Inference Attacks against Large Vision-Language Models (2026.eacl-long)
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| Challenge: | Large vision-language models (LVLMs) are trained on large-scale datasets, which can pose privacy risks if training images contain sensitive information. |
| Approach: | They propose to detect whether a target image is used to train LVLMs by using image-text pairs and single-modality content to detect image-related data. |
| Outcome: | The proposed methods detect whether a target image is used to train the LVLM on large-scale datasets. |
SUA: Stealthy Multimodal Large Language Model Unlearning Attack (2025.emnlp-main)
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| Challenge: | Multimodal Large Language Models (MLLMs) trained on massive data may memorize sensitive personal information and photos, posing privacy and copyright concerns. |
| Approach: | They propose a framework that learns a universal noise pattern to recover unlearned information from MLLMs. |
| Outcome: | The proposed framework learns a universal noise pattern and can reveal unlearned content when applied to images. |
A Functionality-Grounded Benchmark for Evaluating Web Agents in E-commerce Domains (2026.acl-long)
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| Challenge: | Existing benchmarks focus on product search tasks, but ignore potential risks. |
| Approach: | They propose a data generation pipeline that leverages webpage content and interactive elements to create diverse, functionality-grounded user queries. |
| Outcome: | The proposed framework assesses the performance and safety of web agents under dynamic, real-world e-commerce environments. |
MEVER: Multi-Modal and Explainable Claim Verification with Graph-based Evidence Retrieval (2026.eacl-long)
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| Challenge: | Existing methods for verification of claims rely on textual evidence only or ignore the explainability. |
| Approach: | They propose a multi-modal reasoning model that integrates text and visual evidence for verification. |
| Outcome: | The proposed model achieves evidence retrieval, multi-modal claim verification, and explanation generation. |
Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations (2026.findings-acl)
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Haitong Luo, Fali Wang, Weiyao Zhang, Xianren Zhang, Zhiwei Zhang, Tianxiang Zhao, Minhua Lin, Jiahao Zhang, Hui Liu, Xianfeng Tang, Qi He, Suhang Wang, Xuying Meng, Yujun Zhang
| Challenge: | Large language models exhibit intrinsic limitations such as knowledge cutoff, single-threaded reasoning that hinders finer-grained branch and aggregation, and rigid collaboration mechanisms that struggle to coordinate specialized capabilities. |
| Approach: | They propose a taxonomy spanning *Graph-Assisted Knowledge Augmentation*, *Graph Assisted Reasoning and Planning*, and *Graphed LLM Collaboration*. |
| Outcome: | The proposed models show that graphs can augment and correct LLMs and support dynamic coordination among experts and agents in collaborative settings. |