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Linjuan Wu

Linjuan Wu has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Reasoning; often with Weiming Lu, Yongliang Shen, Wenqi Zhang.

3 papers in our library · 2024–2026

OpenAlex · h-index 3 · 11 works · 38 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 48 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7242

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 · China2024–2026 · 10 paperscurrent🏢Xiaohongshu (Little Red Book)Shanghai · China2026 · 1 papercurrent

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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 works

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

 Works by year

2020s

20262 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives (2024 · 75 citations)
    The paper asks why large language models rarely fix their own mistakes when they reflect without outside feedback.
  2. AutoTaskEval: Towards Domain-Specific and Fine-Grained Evaluation for LLMs (2026)
    Static, manually built benchmarks are expensive, cover few tasks, and lose discriminative power as LLMs improve.
  3. Pause or Fabricate? Training Language Models for Grounded Reasoning (2026)
    The paper examines a failure mode the authors call ungrounded reasoning. When a question lacks a needed fact, a language model often notices the gap early but then invents a value and reasons confidently to a wrong answer. The authors argue that the cause is weak awareness of when valid inference is possible, not…

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Self-Contrast: Better Reflection Through Inconsistent Solving PerspectivesAnnual Meeting of the Association for Computational Linguistics (ACL)Wenqi Zhang, Yongliang Shen, Linjuan Wu, et al.2024 · 75 citations
  2. AutoTaskEval: Towards Domain-Specific and Fine-Grained Evaluation for LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Qingqing Lyu, Linjuan Wu, Yongliang Shen, et al.2026
  3. Pause or Fabricate? Training Language Models for Grounded ReasoningFindings of the Association for Computational LinguisticsYiwen Qiu, Linjuan Wu, Yizhou Liu, et al.2026
  4. A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAMΔ Integration into Upcycled MoE ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetHao Zhou et al.2026

 Co-authors here

12 of 16 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 48 times (of the 149,439 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/linjuan-wu/