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Wanli Yang

Wanli Yang has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining; often with Fei Sun, Xueqi Cheng, Hexiang Tan.

3 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 2 · 10 works · 25 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 31 times in our library · ⭐ Paper Stars ranking: #7891

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

🎓Huazhong University of Science and TechnologyWuhan · China2026 · 1 papercurrent🔬Institute of Computing TechnologyBeijing · China2024–2025 · 3 papers🏛️Chinese Academy of SciencesBeijing · China2024 · 2 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 6The 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 · 1 workInformation Systems · 1 work

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

 Works by year

2020s

20261 paper20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts? (2024 · 37 citations)
    Hybrid question-answering systems give an LLM two kinds of context: passages it wrote itself and passages retrieved from an external corpus such as Wikipedia.
  2. The Mirage of Model Editing: Revisiting Evaluation in the Wild (2025 · 9 citations)
    Published model-editing results often look nearly perfect. This paper asks whether those results hold once the evaluation reflects how an edited LLM would actually be used. The authors build QAEdit, a benchmark drawn from Natural Questions, TriviaQA and SimpleQA. GPT-4 adds the subjects and paraphrased prompts that…
  3. BaseCal: Unsupervised Confidence Calibration via Base Model Signals (2026 · 1 citation)
    Post-trained LLMs (PoLLMs), such as instruction-tuned or RLHF-aligned models, tend to be overconfident: they assign high probability even to wrong answers, which limits the usefulness of confidence for detecting hallucinations.

 Co-authors here

11 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 31 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

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