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Shiping Yang

Shiping Yang has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining, Summarization, Evaluation & benchmarks; often with Angel X. Chang, Hengyuan Zhang, Chaofan Tao.

3 papers in our library · 2023–2026

OpenAlex · h-index 1 · 4 works · 2 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 103 times in our library · ⭐ Paper Stars ranking: #7240

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

🎓Simon Fraser UniversityBurnaby · Canada2025–2026 · 5 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 6The 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 workInformation Systems · 1 work

🧭 Topics 4Research subjects and languages the work is about.Evaluation & benchmarks · 1 workSummarization · 1 workInformation retrieval & search · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20262 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Human-like Summarization Evaluation with ChatGPT (2023 · 128 citations)
    Automatic metrics for text summarization, such as ROUGE and embedding-based scores, often disagree with human judgment and offer little interpretability.
  2. Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation (2026 · 13 citations)
    Small LLMs are usually distilled by fine-tuning them on outputs from the most powerful available teacher. Prior work shows this can backfire: very strong teachers may produce answers that are too complex or too far from what the student can absorb. Earlier remedies (Mix, CAR) still make every candidate teacher answer…
  3. Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding Data (2026)
    Retrieval-augmented language models (RALMs) are usually tested against noise that changes a document's meaning.

 Co-authors here

12 of 19 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 103 times (of the 149,439 papers whose bibliographies we read); 27 citing papers with a page here →.

Updated

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