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Ke Ji

Ke Ji has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning, Information retrieval & search; often with Benyou Wang, Junying Chen, Wanlong Liu.

2 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 3 · 12 works · 24 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 95 times in our library · ⭐ Paper Stars ranking: #7813

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

🎓Chinese University of Hong Kong, ShenzhenShenzhen · China2024–2026 · 10 paperscurrent

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 8The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 worksMedicine · 1 workHealth Informatics · 1 workInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Reasoning · 1 workInformation retrieval & search · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs (2024 · 87 citations)
    Work that reproduces OpenAI o1-style reasoning has mostly targeted mathematics. Medicine also needs careful step-by-step reasoning, but a clinical chain of thought is hard to check automatically. The authors address this by building verifiable medical problems. They start from 192K multiple-choice exam questions…
  2. RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions (2025 · 19 citations)
    Retrieval-augmented LLMs are usually tuned on narrow QA sets such as NQ or TriviaQA. As a result, they cope poorly when retrieved passages only partly help, do not help at all, or must be combined across several documents. The authors present RAG-Instruct, a recipe for generating varied RAG training data from any…

 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 95 times (of the 149,439 papers whose bibliographies we read); 38 citing papers with a page here →.

Updated

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