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Shenran Wang

Shenran Wang has 3 papers in our library, in 2026, most of them on Implicature, Language models & pretraining, Question answering; often with Hila Gonen, Jian Zhu, Mengzhe Geng.

3 papers in our library · 2026

OpenAlex · h-index 1 · 6 works · 2 citations OpenAlex, 2026-10-14

📄 3 works in our library · ⭐ Paper Stars ranking: #11851

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 British ColumbiaVancouver · Canada2025–2026 · 4 paperscurrent

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).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 workMedicine · 1 workPhysiology · 1 workSignal Processing · 1 work

🧭 Topics 7Research subjects and languages the work is about.Implicature · 1 workInterpretability · 1 workLanguage models & pretraining · 1 workLow-resource languages · 1 workMultilingual & cross-lingual NLP · 1 workQuestion answering · 1 workSpeech & spoken language · 1 work

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Don't ‘Well, Actually' Me Unless You Know What You're Talking About: Weak Presupposition Verification Degrades General QA Performance (2026)
    The paper examines false-presupposition QA (FPQA), where a model should either answer a question or point out and correct a mistaken assumption inside it.
  2. Exploring Cross-Lingual Voice Conversion Methods for Anonymizing Low-Resource Text-to-Speech (2026)
    Neural TTS for small language communities raises a privacy concern: the few people who record training data are often well known, and a model that can say anything in their voice could harm their reputation or lend credibility to scams.
  3. Understanding In-Context Learning Beyond Transformers: An Investigation of State Space and Hybrid Architectures (2026)
    Mechanistic work on in-context learning (ICL) has so far explained it mainly through particular attention heads in transformers, such as function vector (FV) heads and induction heads.

 Co-authors here

4 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.

Updated

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