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Chris Xing Tian

Chris Xing Tian has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Information retrieval & search; often with Haoliang Li, Hui Liu, Hao Sun.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 3 · 14 works · 56 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 9 times in our library · ⭐ Paper Stars ranking: #9784

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

🔬Peng Cheng LaboratoryShenzhen · China2025–2026 · 4 paperscurrent🏢BGI GroupShenzhen · China2026 · 1 papercurrent🎓City University of Hong KongHong Kong · China2022–2025 · 8 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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).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 2Research subjects and languages the work is about.Information retrieval & search · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning (2025 · 11 citations)
    Retrievers trained to pick in-context learning demonstrations, such as EPR, beat simple heuristics like BM25 or off-the-shelf embeddings.
  2. Domain-Specific Data Generation Framework for RAG Adaptation (2026)
    Off-the-shelf RAG pipelines often perform poorly in specialised domains. Adapting their components, such as the embedding model or the generator LLM, requires domain-grounded training data, which is rarely available. The authors present RAGen, a data-centric framework that turns raw domain documents into…

 Co-authors here

6 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 9 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated

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