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Jiangtong Li

Jiangtong Li has 2 papers in our library, in 2018–2026, most of them on Language models & pretraining; often with Gongshen Liu, Hai Zhao, Zhuosheng Zhang.

2 papers in our library · 2018–2026

OpenAlex · ORCID · h-index 15 · 59 works · 603 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 83 times in our library · ⭐ Paper Stars ranking: #6140

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Tongji UniversityShanghai · China2023–2026 · 27 paperscurrent🏥Shanghai Sunshine Rehabilitation CenterShanghai · China2025–2026 · 4 paperscurrent🎓Shanghai Jiao Tong UniversityShanghai · China2018–2023 · 15 papers🎓Guangxi Normal UniversityGuilin · China2008 · 4 papers🎓Beijing Institute of TechnologyBeijing · China2021–2022 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

 Works by year

2020s

20261 paper

2010s

20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Modeling Multi-turn Conversation with Deep Utterance Aggregation (2018 · 154 citations)
    The paper tackles retrieval-based response selection for multi-turn dialogue. Earlier systems either concatenate the whole history, which adds noise, or match the candidate against each turn without weighing which turns and words matter. The authors propose Deep Utterance Aggregation (DUA), which has five modules:
  2. Can LLMs Really Judge? A Progressive Argumentation-Mining Framework for Distinguishing Understanding from Aggregation (2026)
    The paper argues that benchmark accuracy measures how well LLMs generate answers, not whether they can tell a sound solution from a convincing wrong one.

 Co-authors here

3 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 83 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated

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