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Shining Liang

Shining Liang has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Summarization, Evaluation & benchmarks; often with Ming Gong, Ning Wu, Angel X. Chang.

2 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 8 · 26 works · 229 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 35 times in our library · ⭐ Paper Stars ranking: #8220

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

🏢Microsoft Research AsiaBeijing · China2024–2026 · 3 paperscurrent🎓Jilin UniversityChangchun · China2016–2023 · 15 papers🔬Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin UniversityChangchun Shi · China2019–2023 · 5 papers🏢MicrosoftRedmond · United States2022–2023 · 2 papers🏛️Ministry of Education of the People's Republic of ChinaBeijing · China2020–2022 · 2 papers

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 · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 2 worksEvaluation & benchmarks · 1 workSummarization · 1 workInformation retrieval & search · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Large Language Models are Diverse Role-Players for Summarization Evaluation (2023 · 86 citations)
    Metrics such as BLEU, ROUGE and BERTScore measure surface or embedding similarity to a reference. They often diverge from human judgement, especially for fluent LLM-written summaries. Two outputs can score alike on these metrics yet differ clearly in coherence or interest to readers. The authors propose DRPE (Diverse…
  2. 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

8 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 35 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated

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