I am a PhD student at University of Manchester, supervised by Prof. Sophia Ananiadou. Previously, I worked as a deep learning researcher at Tencent Technology, where I designed deep learning models for binary code similarity detection and binary source code matching. I received my Bachelor’s and Master’s degrees from Shanghai Jiao Tong University, supervised by Prof. Gongshen Liu.

I am deeply interested in the inner workings of LLMs and multimodal LLMs. I believe that gaining a better understanding of their underlying mechanisms will be valuable for designing more effective modules and strategies to enhance their capabilities. My current research focuses on:

a) Understanding LLMs and multimodal LLMs. Exploring how LLMs and multimodal LLMs work. My research investigates the underlying mechanisms of factual knowledge, arithmetic, latent multi-hop reasoning, in-context learning, and visual question answering.

b) Post-training for LLMs. Analyzing LLMs and designing methods to enhance LLMs’ capabilities such as knowledge, arithmetic, multimodal, and reasoning during post-training. I developed the back attention module to improve the latent multi-hop reasoning ability of LLMs.

c) Model editing for LLMs. Identifying and editing the important parameters to reduce hallucination, unfairness, toxicity, and bias in LLMs. I designed the neuron-level model editing technique to mitigate gender bias without hurting the LLM’s existing capabilities.

My email is zepingyu@foxmail.com.

🔥 News

📝 Publications

Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language Models

Zeping Yu, Yonatan Belinkov, Sophia Ananiadou [arxiv: 2502.10835]

Understanding and Mitigating Gender Bias in LLMs via Interpretable Neuron Editing

Zeping Yu, Sophia Ananiadou [arxiv: 2501.14457]

Understanding Multimodal LLMs: the Mechanistic Interpretability of Llava in Visual Question Answering

Zeping Yu, Sophia Ananiadou [arxiv: 2411.10950]

Interpreting Arithmetic Mechanism in Large Language Models through Comparative Neuron Analysis

Zeping Yu, Sophia Ananiadou [EMNLP 2024 (main)]

Neuron-Level Knowledge Attribution in Large Language Models

Zeping Yu, Sophia Ananiadou [EMNLP 2024 (main)]

How do Large Language Models Learn In-Context? Query and Key Matrices of In-Context Heads are Two Towers for Metric Learning

Zeping Yu, Sophia Ananiadou [EMNLP 2024 (main)]

CodeCMR: Cross-modal retrieval for function-level binary source code matching

Zeping Yu, Wenxin Zheng, Jiaqi Wang, Qiyi Tang, Sen Nie, Shi Wu [NeurIPS 2020]

Order matters: Semantic-aware neural networks for binary code similarity detection

Zeping Yu, Rui Cao, Qiyi Tang, Sen Nie, Junzhou Huang, Shi Wu [AAAI 2020]

Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation

Zeping Yu, Jianxun Lian, Ahmad Mahmoody, Gongshen Liu, Xing Xie [IJCAI 2019]

Sliced recurrent neural networks

Zeping Yu, Gongshen Liu [COLING 2018]

📖 Educations

  • 2023.09 - 2027.02, PhD of Computer Science, University of Manchester.
  • 2017.09 - 2020.02, Master of Engineering, Shanghai Jiao Tong University.
  • 2013.09 - 2017.06, Bachelor of Engineering, Shanghai Jiao Tong University.