✨ About me

Hi, everyone! I am currently a first-year PhD student (10.2024-) at King’s College London, NLP group, School of Informatics. I am fortunate to be supervised by Dr. Lin Gui and Prof. Yulan He. I finished my MSC AI at the University of Edinburgh and my BEng EEE project jointly at the University of Edinburgh and North China Electric Power University(NCEPU). I am fortunate to be supervised by Prof. Frank Keller for my MSC and Dr. Jiabin Jia for my BEng.

In addition to research, I interned for four months as a full-stack engineer specialising in voice cloning algorithms at 01.AI.

🔍 Research Summary

My research interests lie in the intersection of Natural Language Processing and Multi-modal Learning, i.e., cross-modality (Vision-language-audio Environment Understanding), text-generation (Open domain/long-form question answering), and reasoning (Embedding planning, latent embedding learning), to enhance the fundamental reliability and multi-modal ability of NLP models. Generally speaking, my goal is to enhance 1) the robust cross-modality alignment and comprehension abilities in interactive communication environments, and 2) advancing knowledge-based large language models (LLMs) and agents to refine reasoning. My long-term objective is to construct a robust AI framework harmonising natural language understanding and generation, dismantling barriers in human-machine communication.

• Effectiveness and Efficiency in Question Answering implemented by large language models, such as EEE-QA, COLING 2024: A gate layer framework in multi-choice question answering, and EmbQA: A rerank-reading system with an unsupervised contrastive learning framework in the rerank and an exploratory embedding mechanism in the reading framework.

• Reasoning in latent space, such as CODI: Train a large language model (LLM) to perform reasoning without explicitly generating Chain-of-Thought (CoT) tokens.

• Multi-modal interpretability and application, such as Causal and temporal inference in videos QA, ACL ALVR 2024, and Human motion video generation

🔥 News

🚀 I am always open to new collaborations and engaging discussions. Feel free to reach out if you are interested in working together or just want to chat!

📝 Publications

ACL 2025 Main

Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering

Zhanghao Hu, Hanqi Yan, Qinglin Zhu, Zhenyi Shen, Yulan He, Lin Gui

[Project]

• Reordering retrieved passages to highlight those most likely to contain correct answers by refining query representations via lightweight linear layers under an unsupervised contrastive learning objective.
• Introduce an exploratory embedding that broadens the model’s latent semantic space to diversify candidate generation and employs an entropy-based selection mechanism to choose the most confident answer automatically

ACL ALVR 2024

Causal and Temporal Inference in Visual Question Generation by Utilizing Pre-trained Models

Zhanghao Hu, Frank Keller

[Project]

• Our study introduces a framework that leverages vision-text matching pre-trained models to guide language models in recognizing event-entity relationships within videos and generating inferential questions.

COLING 2024

EEE-QA: Exploring Effective and Efficient Question-Answer Representations

Zhanghao Hu, Yijun Yang, Junjie Xu*, Yifu Qiu, Pinzhen Chen

[Project]

• This work challenges the existing question-answer encoding convention and explores finer representations. We experiment with different PLMs, and with and without the integration of knowledge graphs. Results prove that the memory efficacy of the proposed techniques is with little sacrifice in performance.

📝 Publications – Preprint

Arxiv 2025.02

CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation

Zhenyi Shen, Hanqi Yan, Linhai Zhang, Zhanghao Hu, Yali Du, Yulan He

[Project]

• CODI (Continuous Chain-of-Thought via Self-Distillation) is a novel framework that distils CoT into a continuous space, where a shared model acts as both teacher and student, jointly learning explicit and implicit CoT while aligning their hidden activation on the token generating the final answer.

TechRxiv 2024.09

Human motion video generation: A survey

Haiwei Xue, Xiangyang Luo, Zhanghao Hu, Xin Zhang, Xunzhi Xiang, Yuqin Dai, Jianzhuang Liu, Zhensong Zhang, Minglei Li, Jian Yang, Fei Ma, Zhiyong Wu, Changpeng Yang, Zonghong Dai, Fei Richard Yu

[Project]

• This paper addresses this gap by providing an in-depth survey of human motion video generation, encompassing over ten sub-tasks, and detailing the five key phases of the generation process: input, motion planning, motion video generation, refinement, and output.

🎖 Honors and Awards

• 2023.01 IBM Shortlist for Best Project in Machine Learning Practical Course, Ranked 5/103.

• 2022.06 Outstanding Graduate Award, NCEPU

• 2022.05 £3000 Scholarship, University of Edinburgh, For Excellent 2+2 International Students

• 2021.05 £2500 Scholarship, University of Edinburgh, For Excellent 2+2 International Students

• 2020.10 Third Prize Academic Scholarship, NCEPU, Awarded to top 10% of students

• 2019.10 Second Prize Academic Scholarship, NCEPU, Awarded to top 5% of students

📖 Educations

• 2022.09 – 2023.11, MSc in Artificial Intelligence, University of Edinburgh, Distinction Degree, ranked top ~10%

• 2020.09 – 2022.05, Bachelor in Electronics and Electrical Engineering, University of Edinburgh, First-Class Honour Degree, ranked top ~10%

• 2018.09 – 2020.06, Bachelor in Electrical Engineering and Its Automation, North China Electric Power University (NCEPU) Ranked ~15%

💬 Invited Talks

💻 Internships

• 2024.04 - 2024.08, Research Intern at 01.AI.