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Xuansheng Wu

Xuansheng Wu has 4 papers in our library, in 2024–2026, most of them on Language models & pretraining, Grammatical error correction & learner language, English; often with Ninghao Liu, Lei Liu, Xiaoming Zhai.

4 papers in our library · 2024–2026

OpenAlex · ORCID · Wikidata · h-index 10 · 25 works · 404 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 115 times in our library · ⭐ Paper Stars ranking: #6136

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

🎓University of GeorgiaAthens · United States2021–2026 · 19 papersEducation 2021–2025 · PhD, School of Computingcurrent

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 8The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArts and Humanities · 2 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 2 worksSafety Research · 2 worksSocial Sciences · 2 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksEnglish · 2 worksGrammatical error correction & learner language · 2 works

💡 Concepts 1Key ideas and methods explained on our concept pages.Artificial intelligence · 1 work

 Works by year

2020s

20262 papers20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. InFoBench: Evaluating Instruction Following Ability in Large Language Models (2024 · 136 citations)
    Judging whether large language models obey user instructions usually relies on pairwise A/B comparisons, Elo ratings or a single holistic score.
  2. Artificial Intelligence Bias on English Language Learners in Automatic Scoring (2025 · 15 citations)
    The paper asks whether automatic scoring of short written science answers treats English Language Learners (ELLs) unfairly, and in particular whether ELLs being underrepresented in training data is the cause.
  3. Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering (2026 · 3 citations)
    Steering large language models by adding linear concept vectors to their hidden states works poorly for abstract concepts such as sycophancy or corrigibility.
  4. BRIDGE the Gap: Mitigating Bias Amplification in Automated Scoring of English Language Learners via Inter-group Data Augmentation (2026)
    Automated scorers for student answers, built on deep models and LLMs, can widen score gaps between student groups beyond the gaps in the human labels they learn from.

 Co-authors here

12 of 17 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 115 times (of the 149,439 papers whose bibliographies we read); 31 citing papers with a page here →.

Updated

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