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Jiahang Xu

Jiahang Xu has 2 papers in our library, in 2024, most of them on Language models & pretraining, Reasoning; often with Fan Yang, Li Lyna Zhang, Mao Yang.

2 papers in our library · 2024

OpenAlex · ORCID · h-index 7 · 19 works · 480 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 198 times in our library · ⭐ Paper Stars ranking: #6293

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

🎓Zhejiang UniversityHangzhou · China2026 · 2 paperscurrent🎓Guangdong University of Foreign StudiesGuangzhou · China2026 · 1 papercurrent🏢MicrosoftRedmond · United States2022–2024 · 6 papers🎓Fudan UniversityShanghai · China2019–2022 · 5 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).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 1 work

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens (2024 · 142 citations)
    Most large language models can only work with a fixed span of text. LLaMA2, for example, is limited to 4096 tokens, and earlier extension methods stopped around 128k. Going further is hard for three reasons: unseen position indices destabilize training, very long training texts are scarce and expensive to use, and…
  2. Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers (2024 · 74 citations)
    Small language models (SLMs) are often weak at multi-step reasoning. The usual fix is fine-tuning on data produced by a stronger model such as GPT-4. Self-improvement methods that avoid a teacher, such as RAP, tend to fail on small models for two reasons: they explore the solution space poorly, and they cannot…

 Co-authors here

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

 Cited by

Papers of our library cite their works 198 times (of the 149,439 papers whose bibliographies we read); 49 citing papers with a page here →.

Updated

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