🧑‍🔬 Chuanyang Zheng

🏛️ Affiliations

🎓Chinese University of Hong KongHong Kong · China2023–2026 · 15 paperscurrent🏢Alibaba GroupHangzhou · China2026 · 1 papercurrent🏛️Chinese Academy of SciencesBeijing · China2022–2025 · 4 papers🔬South China Sea Institute Of OceanologyGuangzhou · China2022–2025 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Progressive-Hint Prompting Improves Reasoning in Large Language Models (2023 · 131 citations)
   Few-shot prompting methods such as Chain-of-Thought (CoT) and self-consistency improve multi-step reasoning in LLMs, but they treat each answer as final and never feed it back to the model.
2. 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…
3. FIMO: A Challenge Formal Dataset for Automated Theorem Proving (2023 · 53 citations)
   Neural provers built on large language models still rarely solve olympiad-grade problems. Formal data is scarce and costly to produce, existing benchmarks contain few truly hard items, and most formal datasets lack natural-language counterparts. The authors address this with FIMO, a Lean benchmark drawn from the IMO…
4. 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

6 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

1. Progressive-Hint Prompting Improves Reasoning in Large Language ModelsarXiv:2304.09797Chuanyang Zheng, Zhengying Liu, Enze Xie, et al.2023 · 131 citations
2. LEGO-Prover: Neural Theorem Proving with Growing LibrariesarXiv:2310.00656Haiming Wang, Huajian Xin, Chuanyang Zheng, et al.2023 · 79 citations
3. FIMO: A Challenge Formal Dataset for Automated Theorem ProvingarXiv:2309.04295Chengwu Liu, Jianhao Shen, Huajian Xin, et al.2023 · 53 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. Lyra: Orchestrating Dual Correction in Automated Theorem Proving ↗arXiv:2309.15806 · no summary yetChuanyang Zheng et al.2023 · 24 citations
6. 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 21 co-authors with a page, the most papers together first.

• Enze Xie ×3
• Haiming Wang ×3
• Zhengying Liu ×3
• Huajian Xin ×2
• Jing Xiong ×2
• Lin Li ×2
• Qingxing Cao ×2
• Yichun Yin ×2
• Zhenguo Li ×2
• Chengming Li
• Han Shi
• Jian Yin

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

🔗 Cited by

Papers of our library cite their works 276 times (of the 149,439 papers whose bibliographies we read); 48 citing papers with a page here →.

Updated 2 November 2026

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