Her research focuses on Efficient and Sustainable AI Systems, particularly data–model–hardware co-design and hardware-aware, energy-efficient LLM inference. She also explores Reliable and Responsible AI, including multilingual fact verification, hallucination detection and mitigation, trustworthy AI benchmarking, and AI for scientific discovery.
Selected publications
2026
B. Fan*, M. Li*, H. Zhang*, S. Dong, N. P. Mareedu, X. Liu, W. Shi, Y. Feng, Y. Huang, and H. Fan.
Towards Visual Query Segmentation in the Wild.
Advances in Neural Information Processing Systems (NeurIPS), 2026.
@misc{fan2026visualquerysegmentationwild,
title={Towards Visual Query Segmentation in the Wild},
author={Bing Fan and Minghao Li and Hanzhi Zhang and Shaohua Dong and Naga Prudhvi Mareedu and Xiaoqiong Liu and Weishi Shi and Yunhe Feng and Yan Huang and Heng Fan},
year={2026},
eprint={2603.08898},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2603.08898},
}
In this paper, we introduce visual query segmentation (VQS), a new paradigm of visual query localization (VQL) that aims to segment all pixel-level occurrences of an object of interest in an untrimmed video, given a visual query. Compared with existing VQL locating only the last appearance of a target using bounding boxes, VQS enables more comprehensive (i.e., all object occurrences) and precise (i.e., pixel-level masks) localization, making it more practical for real-world scenarios. To foster research on this task, we introduce VQS-4K, a large-scale benchmark dedicated to VQS. Specifically, VQS-4K consists of 4,111 videos with more than 1.3 million frames, and covers a diverse set of 222 object categories. Each video is paired with a visual query defined by a frame outside search video and its target mask, and annotated with spatial-temporal masklets corresponding to the queried target. To ensure high quality, all videos in VQS-4K are manually labeled with meticulous inspection and iterative refinement. To our best knowledge, VQS-4K is the first benchmark specifically designed for VQS. Furthermore, to stimulate future research, we present a simple yet effective method, named VQ-SAM, which extends SAM 2 by leveraging target-specific and background distractor cues from the video to progressively evolve the memory through a novel multi-stage framework with an adaptive memory generation (AMG) module for VQS, significantly improving the performance. In our experiments on VQS-4K, VQ-SAM achieves promising results and surpasses existing methods, evidencing its effectiveness. With VQS-4K and VQ-SAM, we expect to inspire more future research and applications on VQS. Our benchmark and code are released at https://github.com/VAIL-Fan/VQS.
H. Zhang, S. Anjum, H. Fan, W. Zheng, Y. Huang, and Y. Feng.
Poly-FEVER: A Multilingual Hallucination Detection Benchmark.
Findings of the Association for Computational Linguistics (EMNLP Findings), 2026.
@inproceedings{zhang-etal-2026-poly,
title = "Poly-{FEVER}: A Multilingual Hallucination Detection Benchmark",
author = "Zhang, Hanzhi and
Anjum, Sumera and
Fan, Heng and
Zheng, Weijian and
Huang, Yan and
Feng, Yunhe",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.540/",
doi = "10.18653/2026.findings-emnlp.540",
pages = "11353--11369",
ISBN = "979-8-89176-462-0",
abstract = "We present Poly-FEVER, an open-source large-scale multilingual benchmark for hallucination detection through controlled factuality judgment in large language models. Poly-FEVER extends FEVER, Climate-FEVER, and SciFact to 77,973 Supported/Refuted claims aligned across 11 languages. We augment each claim with topic metadata from a 22-topic LDA model, enabling topic-aware analysis of multilingual factuality behavior. Translations are produced with Google Cloud Translation and validated by a targeted human audit, GEMBA, and COMETKiwi. We benchmark ChatGPT-3.5, LLaMA-2/3, Phi-3.5, and Mistral across three prompt families and evaluate robustness to model-generated rephrasing. We further analyze resource imbalance using web presence as a proxy for online claim availability, and evaluate retrieval-augmented factuality judgment with DPR over Wikipedia. Results show pronounced cross-lingual disparities under aligned claims, with stronger accuracy in high-resource languages such as English and Chinese and weaker accuracy in lower-resource languages such as Amharic and Tamil. Topic-aware prompting and retrieval provide selective gains, especially in Arabic and Amharic, while their effects vary across languages and can conflict with model priors. Poly-FEVER establishes a rigorous resource for responsible, language-adaptive hallucination detection and language-sensitive factuality evaluation. The proposed Poly-FEVER is available at: https://huggingface.co/datasets/HanzhiZhang/Poly-FEVER."
}
We present Poly-FEVER, an open-source large-scale multilingual benchmark for hallucination detection through controlled factuality judgment in large language models. Poly-FEVER extends FEVER, Climate-FEVER, and SciFact to 77,973 Supported/Refuted claims aligned across 11 languages. We augment each claim with topic metadata from a 22-topic LDA model, enabling topic-aware analysis of multilingual factuality behavior. Translations are produced with Google Cloud Translation and validated by a targeted human audit, GEMBA, and COMETKiwi. We benchmark ChatGPT-3.5, LLaMA-2/3, Phi-3.5, and Mistral across three prompt families and evaluate robustness to model-generated rephrasing. We further analyze resource imbalance using web presence as a proxy for online claim availability, and evaluate retrieval-augmented factuality judgment with DPR over Wikipedia. Results show pronounced cross-lingual disparities under aligned claims, with stronger accuracy in high-resource languages such as English and Chinese and weaker accuracy in lower-resource languages such as Amharic and Tamil. Topic-aware prompting and retrieval provide selective gains, especially in Arabic and Amharic, while their effects vary across languages and can conflict with model priors. Poly-FEVER establishes a rigorous resource for responsible, language-adaptive hallucination detection and language-sensitive factuality evaluation. The proposed Poly-FEVER is available at: https://huggingface.co/datasets/HanzhiZhang/Poly-FEVER.
H. Zhang, H. Fan, K. Sha, Y. Huang, and Y. Feng.
DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference Acceleration.
Findings of the Annual Meeting of the Association for Computational Linguistics (ACL Findings), 2025.
@inproceedings{zhang-etal-2025-dam,
title = "{DAM}: Dynamic Attention Mask for Long-Context Large Language Model Inference Acceleration",
author = "Zhang, Hanzhi and
Fan, Heng and
Sha, Kewei and
Huang, Yan and
Feng, Yunhe",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.242/",
doi = "10.18653/v1/2025.findings-acl.242",
pages = "4663--4676",
ISBN = "979-8-89176-256-5",
abstract = "Long-context understanding is crucial for many NLP applications, yet transformers struggle with efficiency due to the quadratic complexity of self-attention. Sparse attention methods alleviate this cost but often impose static, predefined masks, failing to capture heterogeneous attention patterns. This results in suboptimal token interactions, limiting adaptability and retrieval accuracy in long-sequence tasks. This work introduces a dynamic sparse attention mechanism that assigns adaptive masks at the attention-map level, preserving heterogeneous patterns across layers and heads. Unlike existing approaches, our method eliminates the need for fine-tuning and predefined mask structures while maintaining computational efficiency. By learning context-aware attention structures, it achieves high alignment with full-attention models, ensuring minimal performance degradation while reducing memory and compute overhead. This approach provides a scalable alternative to full attention, enabling the practical deployment of large-scale Large Language Models (LLMs) without sacrificing retrieval performance. DAM is available at: \url{https://github.com/HanzhiZhang-Ulrica/DAM}."
}
Long-context understanding is crucial for many NLP applications, yet transformers struggle with efficiency due to the quadratic complexity of self-attention. Sparse attention methods alleviate this cost but often impose static, predefined masks, failing to capture heterogeneous attention patterns. This results in suboptimal token interactions, limiting adaptability and retrieval accuracy in long-sequence tasks. This work introduces a dynamic sparse attention mechanism that assigns adaptive masks at the attention-map level, preserving heterogeneous patterns across layers and heads. Unlike existing approaches, our method eliminates the need for fine-tuning and predefined mask structures while maintaining computational efficiency. By learning context-aware attention structures, it achieves high alignment with full-attention models, ensuring minimal performance degradation while reducing memory and compute overhead. This approach provides a scalable alternative to full attention, enabling the practical deployment of large-scale Large Language Models (LLMs) without sacrificing retrieval performance. DAM is available at: https://github.com/HanzhiZhang-Ulrica/DAM.
Hanzhi Zhang’s personality, as an ENTP-A, is defined by her insatiable curiosity and robust intellectual flexibility. She revels in intellectual challenges and is the quintessential devil’s advocate, skillfully deconstructing arguments and occasionally challenging her own beliefs by exploring alternative viewpoints.
Hanzhi pursues an intriguing blend of interests. She delves into the mystical realms of tarot and astrology, using them as unique tools to influence decisions in programming and research. She also enjoys unraveling the latest gossip and embarks on quests to locate lost items.
Her cultural appetites are satiated by frequent visits to musicals, dramas, museums, galleries, and exhibitions, which enrich her understanding of diverse perspectives.
Photography is another passion of hers, which she humorously describes as a sport. This hobby sees her traversing mountains, laden with heavy gear, tirelessly capturing moments through day and night—an endurance exercise that combines her love for nature and artistry.