Qinglin Zhu <br /> (祝清麟)

Qinglin Zhu
(祝清麟)

King's College London
伦敦国王学院

👨‍🎓 About Me

I am a second-year PhD student at King’s College London. I am fortunate to be supervised by Lecturer Lin Gui and Professor Yulan He. My current research focuses on Large Language Model Reasoning and Narrative Understanding in Natural Language Processing (NLP).

I completed both my Bachelor’s and Master’s degrees in Computer Science and Technology at Harbin Institute of Technology (Shenzhen), under the guidance of Professor Ruifeng Xu. During my master’s studies, I engaged in research focused on Stance Detection and Argument Mining.

Besides research, I have interned at Tencent Music Entertainment for nine months and Shopee for four months, and worked at Baidu for five months as a Natural Language Processing Algorithm Engineer. I am currently interning at Microsoft Research Asia in Beijing.

🔥 News

📝 Publications

(* Equal contribution, † Corresponding author)

🤔 Reasoning

ICML-2025 Spotlight
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Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration GitHub
Qinglin Zhu, Runcong Zhao, Hanqi Yan, Yulan He, Yudong Chen, Lin Gui.

  • We propose an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines
  • (1) Embedding perturbation for controlled exploration and
  • (2) Bayesian optimisation to refine embeddings via a verifier-guided objective, balancing exploration and exploitation.
ACL-2025
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Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering
Zhanghao Hu, Hanqi Yan, Qinglin Zhu†, Zhenyi Shen, Yulan He, Lin Gui.

  • Proposes EmbQA, an embedding-level framework for open-domain QA that optimizes retrieval with unsupervised contrastive learning and improves answer diversity via exploratory embeddings.
EMNLP-2025
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Sparse Activation Editing for Reliable Instruction Following in Narratives
Runcong Zhao, Chengyu Cao, Qinglin Zhu, Xiucheng Lv, Shun Shao, Lin Gui, Ruifeng Xu, Yulan He.

  • We introduce Concise-SAE, a training-free method that improves instruction following by editing relevant neurons using natural language instructions.
  • Tested on our new FreeInstruct benchmark of 1,212 narrative-rich examples, it achieves state-of-the-art results without training or labelled data.
ACL-2024
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Mirror: A Multiple-perspective Self-Reflection Method for Knowledge-rich Reasoning
Hanqi Yan*, Qinglin Zhu* , Xinyu Wang, Lin Gui, Yulan He. GitHub

  • Proposes Mirror, enabling LLMs to reflect from multiple perspectives via Navigator–Reasoner cooperation.
  • Encourages both diverse and consistent reasoning to overcome self-reflection traps.
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Spectrum Projection Score: Aligning Retrieved Summaries with Reader Models in Retrieval-Augmented Generation
Zhanghao Hu, Qinglin Zhu, Siya Qi, Yulan He, Hanqi Yan, Lin Gui.

  • Proposes SPS, a supervision-free metric to assess semantic alignment between retrieved summaries and LLM representations.
  • Introduces xCompress, an inference-time controller that ranks and compresses retrievals to improve generation and clarify retrieval–generation interaction.
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📚 Narrative Understanding

ACL-2024 Findings
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Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives
Runcong Zhao*, Qinglin Zhu* , Hainiu Xu, Jiazheng Li, Yuxiang Zhou, Yulan He, Lin Gui. GitHub

  • Existing datasets for narrative understanding often fail to represent the complexity and uncertainty of relationships in real-life social scenarios.
  • To address this gap, we introduce a new benchmark, Conan, designed for extracting and analysing intricate character relation graphs from detective narratives.
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PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games
Qinglin Zhu, Runcong Zhao, Jinhua Du, Lin Gui, Yulan He.

  • We propose PLAYER*, a novel framework for Murder Mystery Games (剧本杀) using an anytime sampling-based planner and a questioning-driven search framework.
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SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding
Runcong Zhao, Qinglin Zhu*, Hainiu Xu, Bin Liang, Yulan He, Lin Gui.

  • Proposes SymbolicThought, a human-in-the-loop system combining LLM extraction and symbolic reasoning for character relationship understanding.
  • Supports editable relationship graphs, logical constraints, and interactive conflict resolution.

😆 Sentiment Analysis and Stance Detection

🗣️ Argumentation Mining and Sequence Labeling

🏅 Honors and Awards

  • 2023.03 Outstanding Master’s Graduate & Outstanding Dissertation for Master’s Degree, HIT (3%)
  • 2023.03 Sailvan Times Scholarship (2%), Harbin Institute of Technology
  • 2021.10 National Scholarship (for Graduate Student, 2%)
  • 2021.10 Pacemaker to Merit Student (2%), Harbin Institute of Technology
  • 2021.03 NLPCC-2021 Shared Task: Argument Pair Extraction (Top 1)
  • 2021.02 SemEval-2021 Task 5 Competition: Toxic Spans Detection (Top 1, Team Leader)
  • 2020.06 Outstanding Bachelor’s Graduate & Outstanding Dissertation for Bachelor’s Degree, HIT (3%)
  • 2019.10 National Scholarship (for Undergraduate Student, 2%)

📖 Educations

💻 Works and Internships

  • 2025.05 - Present, Microsoft Research Asia , Beijing, China.(Internship)
  • 2023.04 - 2023.08, Baidu , Shanghai, China.
  • 2022.06 - 2022.09, Shopee , Shanghai, China.(Internship)
  • 2021.08 - 2022.04, Tencent Music Entertainment Group , Shenzhen, China.(Internship)

🫡 Service

  • Reviewer: ICML, ACL, EMNLP, NAACL, NLPCC.

Last Updated:

Aug 21, 2025