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Liangyue Li

Liangyue Li has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning, Efficient models & compression; often with Zulong Chen, Chenglin Li, Defu Lian.

2 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 13 · 62 works · 631 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 11 times in our library · ⭐ Paper Stars ranking: #9809

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

🏢Alibaba GroupHangzhou · China2021–2026 · 23 paperscurrent🎓Arizona State UniversityTempe · United States2014–2018 · 16 papers🏢AmazonSeattle · United States2019–2021 · 11 papers🎓Northeastern UniversityBoston · United States2013–2014 · 6 papers🏢Alibaba GroupSan Mateo · United States2021–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Efficient models & compression · 1 workLanguage models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Mixed Distillation Helps Smaller Language Model Better Reasoning (2023 · 11 citations)
    Large language models reason well but are expensive to run. Earlier work on distilling reasoning into compact models mostly transfers only natural-language Chain of Thought (CoT). This paper argues that Program of Thought (PoT), where the model writes executable Python, is a valuable extra training signal. The…
  2. Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration (2026)
    Generative listwise rerankers built on LLMs suffer from position bias: their output depends on where a document sits in the prompt, not only on its relevance.

 Co-authors here

10 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 11 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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