PeopleofSuperintelligence

Researcher

Jialong Tang

Also known as 唐家龙

Jialong Tang is a researcher at Alibaba Tongyi in Hangzhou who works on Qwen, large language models and multilingual AI. After a bachelor's degree at Xiamen University in 2018, he completed a PhD in 2023 at the Chinese Information Processing Laboratory of the Institute of Software, Chinese Academy of Sciences, under Le Sun and Xianpei Han. His early papers covered aspect-level sentiment classification at ACL 2019 and end-to-end event extraction with Text2Event in 2021. He coauthored the Qwen2 and Qwen3 technical reports in 2024 and 2025, along with PolyMath, a benchmark of mathematical reasoning in 18 languages. In 2024, his team won first place in the WMT 2024 shared tasks on translation into low-resource languages of Spain and on literary translation.

Now at
Alibaba

Timeline

  1. 2018–2019

    Sentiment analysis at Xiamen

    He earns a bachelor's degree at Xiamen University in 2018, supervised by Professor Jinsong Su. In July 2019, he is lead author of an ACL paper in Florence that proposes progressive self-supervised attention learning for aspect-level sentiment classification.

  2. 2021

    Text2Event for event extraction

    In 2021, he coauthors Text2Event with Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun and others at ACL-IJCNLP 2021. The method extracts events from text end to end through sequence-to-structure generation.

  3. 2023

    PhD at the Institute of Software

    He completes a PhD at the Chinese Information Processing Laboratory of the Institute of Software, Chinese Academy of Sciences, in 2023, supervised by Le Sun and Xianpei Han. In May 2023, he is first author of a paper on ESHer, an Event Schema Harvester that induces event schemas from pre-trained language models.

  4. July 2024

    The Qwen2 technical report

    In July 2024, he is among the authors of the Qwen2 Technical Report. The Qwen2 series spans language models from 0.5 to 72 billion parameters, with open weights on Hugging Face and ModelScope.

  5. 2024

    First place at WMT 2024

    His team wins first place in the WMT 2024 shared tasks for translation into low-resource languages of Spain and for literary translation. For the Spanish task, the system translates Spanish into Aragonese, Aranese and Asturian, building on a fine-tuned Qwen2-0.5B model.

  6. April 2025

    The PolyMath benchmark

    In April 2025, he coauthors PolyMath, a multilingual mathematical reasoning benchmark covering 18 languages and four difficulty levels, which appears in the NeurIPS 2025 Datasets and Benchmarks Track. Qwen-3-235B-A22B-Thinking and Gemini-2.5-pro score 54.6 and 52.2 on it.

  7. May 2025

    The Qwen3 technical report

    He coauthors the Qwen3 Technical Report in May 2025. Qwen3 joins thinking and non-thinking modes in one framework, ranges from 0.6 to 235 billion parameters, and expands multilingual support from 29 to 119 languages and dialects.

  8. As of October 2026

    Researcher at Alibaba Tongyi

    He works at Alibaba Tongyi in Hangzhou, where his research covers Qwen, large language models and multilingual work. In October 2025, he coauthors the Qwen3Guard Technical Report on multilingual safety guardrail models. Qwen-Scope, an open-source suite of sparse autoencoders for Qwen3 and Qwen3.5 models that he coauthors, follows in May 2026.

Career

  1. NowAlibabaResearcher

Education

  1. 2023Institute of Software, Chinese Academy of SciencesPhD
  2. 2018Xiamen UniversityBachelor's degree

From the biography.

  1. Google alumni have founded or led 133 other organizations, more than any other AI labIncludes Alibaba. We estimate that alumni of 17 major AI labs went on to found or lead 350 other organizations, including 226 they founded, and we trace 472 career moves directly between 23 labs since 2010.
  2. Universities (31%) and startups and other companies (25%) employ the largest shares of 2020–2026 entrantsIncludes Alibaba. Among 819 people whose first recorded role started in 2020–2026 and who have a known current organization, 60% work in commercial sectors (25% at startups and other companies, 23% at frontier labs and 13% at big tech, with 9 in two), and 31% work at universities, compared with 66% for earlier entrants.
  3. The top 10 organizations employed 22% of people shaping AI in 2025: 51% of company researchers and 21% of university researchersIncludes Alibaba. We estimate the share of people shaping AI who worked at the top 10 organizations each year from 1955 to 2025, comparing concentration across companies, universities, and institutes and labs.
  4. 16% of AI career moves in the 2020s went from a university to a companyIncludes Alibaba. We estimate that 16% of AI career moves in the 2020s went from a university to a company; 29% of moves starting at a university went to a company, and 76% ended the university role.

Sources and credits

Photo: We have no published photo of Jialong Tang yet. Send us a free-licensed photo.

  1. Jialong Tang's Homepage tangjialong.github.io. Checked 20 October 2026.
  2. Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis - ACL Anthology aclanthology.org. Checked 20 October 2026.
  3. Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction - ACL Anthology aclanthology.org. Checked 20 October 2026.
  4. [2305.07280] Harvesting Event Schemas from Large Language Models arxiv.org. Checked 20 October 2026.
  5. [2407.10671] Qwen2 Technical Report arxiv.org. Checked 20 October 2026.
  6. SJTU System Description for the WMT24 Low-Resource Languages of Spain Task - ACL Anthology aclanthology.org. Checked 20 October 2026.
  7. [2504.18428] PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts arxiv.org. Checked 20 October 2026.
  8. PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts papers.neurips.cc. Checked 20 October 2026.
  9. [2505.09388] Qwen3 Technical Report arxiv.org. Checked 20 October 2026.
  10. [2510.14276] Qwen3Guard Technical Report arxiv.org. Checked 20 October 2026.
  11. [2605.11887] Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models arxiv.org. Checked 20 October 2026.