Kaiqiang Song


Senior Research Scientist

Tencent AI Lab


Kaiqiang Song (宋凯强) is a Senior Research Scientist of Natural Language Processing at Tencent AI Lab, Seattle. His research interests center on advancing artificial intelligence (AI) through fundamental research in machine learning (ML), natural language processing (NLP), and large language models (LLM). Specifically, he’s driven to create AI systems that not only push the boundaries of model architectures, training, and inference methods but also have practical applications, such as text summarization and text generation.

He’s passionate about optimizing model architectures and training strategies to enhance AI performance. Moreover, he’s dedicated to harnessing the creative potential of AI for applications like automated content summarization and personalized text generation. His goal is to contribute to both the theoretical underpinnings of AI and its tangible, real-world impact.

In summary, his research pursuits revolve around the synergy of foundational AI research and its practical applications, with a strong focus on innovation in ML, NLP, LLM, and their role in revolutionizing information processing and content generation.

  • Artificial General Intelligence
  • Natural Language Processing
  • Large Language Models
  • PhD in Computer Science, 2021

    University of Central Florida

  • BS in Computer Science, 2016

    Fudan University


Senior Research Scientist
May 2021 – Present Washington
  • Develop Fundamental and impactful technologies for ML, NLP, and LLM
  • Lead Summarization Research and provide the relative applications support including (e.g. VooV Meeting, QQ Web-Browser, Effidit, and Sougou Baike)
  • Participate in developing Tencent Foundation Model HunyuanAide (a ChatGPT-like AI-Bot)
  • Managing computation and storage resources for Tencent Seattle AI Lab
  • Communicate with Infrastructure and Platform Teams for better support of daily research
  • Win 2022-H2 & 2023-H1 Tencent AI Lab SEVP Award
Research Intern
May 2020 – August 2020 Washington
  • Working on podcast summarization challenge (TREC2020) under the guidance of Dr. Chen Li, Dr. Xiaoyang Wang and Dr. Dong Yu at Tencent AI Lab Seattle Group
  • Won the 1st place in automatic evaluation and 2nd place in human evaluation
Robert Bosch LLC
Research Intern
Robert Bosch LLC
May 2019 – August 2019 California
  • Working on text summarization and Information Retrieval tasks under the guidance of Dr. Bingqing Wang and Dr. Zhe Feng at Bosch Corporate Research, Human Machine Interface Group 2 (CR/HMI-2).
  • Published the AAAI-20 paper: Controlling the Amount of Verbatim Copying in Abstractive Summarization

Recent Publications

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(2023). Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning via Compositional Operations. In EMNLP 2023.

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(2023). DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4. In EMNLP 2023.

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(2023). PIVOINE: Instruction Tuning for Open-world Information Extraction. In EMNLP 2023 (Findings).

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(2023). MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning. In ArXiv.org.

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(2023). Unsupervised Multi-document Summarization with Holistic Inference. In IJCNLP-AACL 2023 (Findings).

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If you would like to get in touch with me or if there’s anyone who wishes to connect with me, please don’t hesitate to send me an email.

  • kqsong2014@gmail.com
  • 929 108th Ave NE, Bellevue, WA 98004
  • Enter Building and take the elevator to Office 1100 on Floor 11