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Xianjie Wu

Xianjie Wu has 4 papers in our library, in 2026, most of them on Language models & pretraining, Question answering, Multilingual & cross-lingual NLP; often with Chao Xue, Chenyao Lu, Di Liang.

4 papers in our library · 2026

OpenAlex · h-index 0 · 4 works · 0 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 16 times in our library · ⭐ Paper Stars ranking: #10548

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

🏢TencentShenzhen · China2026 · 2 paperscurrent🎓Beihang UniversityBeijing · 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 · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 2 works

🧭 Topics 4Research subjects and languages the work is about.Reasoning · 1 workLanguage models & pretraining · 1 workMultilingual & cross-lingual NLP · 1 workQuestion answering · 1 work

 Works by year

2020s

20264 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026 · 8 citations)
    Generative reward models (GRMs) judge LLM outputs using chain-of-thought (CoT) reasoning. They have two weaknesses. They spend reasoning effort on every input, including easy ones. They also pick among sampled reasoning chains by voting, which cannot separate sound chains from ones that only look convincing. The…
  2. Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026 · 6 citations)
    Supervised fine-tuning (SFT) is usually treated as a reliable way to specialize LLMs. The authors show, however, that converged models still get part of their own training set wrong. They call this the Incomplete Learning Phenomenon (ILP), distinguish it from catastrophic forgetting and machine unlearning, and…
  3. M3TQA: Massively Multilingual Multitask Table Question Answering (2026 · 2 citations)
    Research on table question answering with LLMs has focused mostly on English. The few multilingual table benchmarks that exist mainly cover Indo-European or otherwise prominent languages, and they are too small for fine-grained cross-lingual analysis. M3TQA addresses this gap. It starts from 50 real-world tables (41…
  4. Table-R1: Region-based Reinforcement Learning for Table Understanding (2026)
    Large language models still have difficulty answering questions over tables because answers depend on how rows and columns relate.

 Co-authors here

12 of 23 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 16 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

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