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Xin Cheng

Xin Cheng has 3 papers in our library, in 2023–2024, most of them on Language models & pretraining, Text generation, Information retrieval & search; often with Bang Liu, Chaojun Wang, Di Luo.

3 papers in our library · 2023–2024

OpenAlex · h-index 2 · 3 works · 10 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 192 times in our library · ⭐ Paper Stars ranking: #6104

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

🎓Renmin University of ChinaBeijing · China2024 · 1 papercurrent

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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksDecision Sciences · 1 workInformation Systems and Management · 1 workInformation Systems · 1 work

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

 Works by year

2020s

20242 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. AFlow: Automating Agentic Workflow Generation (2024 · 95 citations)
    Agentic workflows for LLMs are structured chains of model calls with instructions, and building them usually takes a lot of manual engineering.
  2. Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory (2023 · 76 citations)
    Retrieval-augmented generators condition on examples pulled from a fixed corpus, usually the training set, so the quality of their memory is capped by what that corpus contains.
  3. Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models (2024 · 65 citations)
    Instruction-tuning data for LLMs is usually derived from seed prompts or existing datasets (FLAN, Self-Instruct, Evol-Instruct), so its topical range is limited by those starting points.

 Co-authors here

12 of 23 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 192 times (of the 149,439 papers whose bibliographies we read); 52 citing papers with a page here →.

Updated

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