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Qingxing Cao

Qingxing Cao has 2 papers in our library, in 2023, most of them on Reasoning, Language models & pretraining, Information retrieval & search; often with Chuanyang Zheng, Enze Xie, Haiming Wang.

2 papers in our library · 2023

OpenAlex · ORCID · h-index 10 · 24 works · 593 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 141 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #7814

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

🎓Sun Yat-sen UniversityGuangzhou · China2015–2025 · 18 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).Computer Science · 2 worksArtificial Intelligence · 2 worksComputational Theory and Mathematics · 1 workArts and Humanities · 1 workInformation Systems · 1 workLanguage and Linguistics · 1 work

🧭 Topics 4Research subjects and languages the work is about.Reasoning · 2 worksEfficient models & compression · 1 workInformation retrieval & search · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. LEGO-Prover: Neural Theorem Proving with Growing Libraries (2023 · 79 citations)
    Formal theorem proving is still hard for large language models. Existing LLM-based provers often fail on competition problems at middle-school level. These "plain" provers write each proof as one long monolithic block and treat the available theorem library as fixed. So a lemma proved for one problem cannot help with…
  2. DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning (2023 · 27 citations)
    The order and choice of demonstrations in a prompt strongly affect how well large language models solve multi-step reasoning problems.

All works

5 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Attention-Aware Face Hallucination via Deep Reinforcement Learning ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetQingxing Cao et al.2017 · 235 citations
  2. LEGO-Prover: Neural Theorem Proving with Growing LibrariesarXiv:2310.00656Haiming Wang, Huajian Xin, Chuanyang Zheng, et al.2023 · 79 citations
  3. MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data ↗arXiv:2402.08957 (ICLR 2024 spotlight) · no summary yetYinya Huang et al.2024 · 29 citations
  4. DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context LearningarXiv:2310.02954Jing Xiong, Zixuan Li, Chuanyang Zheng, et al.2023 · 27 citations
  5. TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models ↗Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yetJing Xiong et al.2023 · 21 citations

 Co-authors here

12 of 16 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 141 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

Updated

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