About Me
I am Cass Zhixue Zhao, a lecturer in Natural Language Processing at the Computer Science Department of the University of Sheffield. My long-term research goal is to enable trustworthy, responsible, and efficient NLP models. These days, I am interested in anything related to interpretability and large language models (LLMs). My recent research projects focus on model compression, model editing, and text-to-image models.
Previously, I worked as a Postdoc researcher on explainable AI and responsible AI. The overarching aim is to demystify predictions made by black-box LLMs, making them easier to understand and trustworthy. The work also addresses model hallucination to ensure the reliability of LLMs, alongside exploring model compression techniques that mitigate compute demands and thus foster inclusivity within NLP research. Back in 2020, I worked as a research assistant within the same department, working on NIHR-funded NLP projects for systematic reviews of public health research. My Ph.D. research, which was funded by the University of Sheffield, looked at transfer learning and mitigating model bias for hate speech detection.
Test TestI am looking for highly motivated PhD students. One funded PhD positions for 3.5 years will be available in late 2026, UKRI rate. Welcome to contact me with your CV ready. CSC or Self-funding with your own topic is welcome too.
Test TestSelected Publications
ICML2025 Rulebreakers Challenge: Revealing a Blind Spot in Large Language Models’ Reasoning with Formal Logic.
Jason Chan, Robert Gaizauskas, Zhixue Zhao
ICML2025 Position: Editing Large Language Models Poses Serious Safety Risks.
Paul Youssef, Zhixue Zhao, Daniel Braun, Jörg Schlötterer, Christin Seifert
ICLR2025 ScImage: How good are multimodal large language models at scientific text-to-image generation?
Leixin Zhang, Yinjie Cheng, Weihe Zhai, Steffen Eger, Jonas Belouadi, Fahimeh Moafian, Zhixue Zhao.
ACL2025 Main Preprint coming soon. Knowledge Image Matters: Improving Knowledge-Based Visual Reasoning with Multi-Image Large Language Models
Guanghui Ye, Huan Zhao, Zhixue Zhao, Xupeng Zha, Yang Liu, Zhihua Jiang
ACL2025 Findings Preprint coming soon. Explainable Hallucination through Natural Language Inference Mapping
Wei-Fan Chen, Zhixue Zhao, Akbar Karimi, Lucie Flek
NAACL2025 Main How to Make LLMs Forget: On Reversing In-Context Knowledge Edits
Paul Youssef, Zhixue Zhao, Jörg Schlötterer, Christin Seifert.
NAACL2025 Main Oral Has this Fact been Edited? Detecting Knowledge Edits in Language Models?
Paul Youssef, Zhixue Zhao, Jörg Schlötterer, Christin Seifert.
TACL2024 Vol. 12 Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization.
George Chrysostomou, Zhixue Zhao, Miles Williams, and Nikolaos Aletras.
NAACL 2024 Main (oral presentation) Comparing Explanation Faithfulness between Multilingual and Monolingual Fine-tuned Language Models.
Zhixue Zhao and Nikolaos Aletras.
ACL 2023 Main [Oral Presentation (the 1st talk was ours)] Incorporating Attribution Importance for Improving Faithfulness Metrics.
Zhixue Zhao and Nikolaos Aletras.
EMNLP 2022 Findings On the Impact of Temporal Concept Drift on Model Explanations.
Zhixue Zhao, George Chrysostomou, Kalina Bontcheva, and Nikolaos Aletras.
(More papers in publications.)
Test Test TestProject
- Co-PI of ExU