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Yizhen Jia

Yizhen Jia has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning; often with Jipeng Zhang, Renjie Pi, Rui Pan.

2 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 10 · 21 works · 239 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 55 times in our library · ⭐ Paper Stars ranking: #9171

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

🎓Nanyang Normal UniversityNanyang · China2025 · 2 paperscurrent🎓Beihang UniversityBeijing · China2019–2022 · 10 papers🎓University of JinanJinan · China2016–2018 · 4 papers🎓Northwestern UniversityEvanston · United States2014–2016 · 2 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 5The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksComputational Theory and Mathematics · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts (2024 · 51 citations)
    TheoremLlama tackles a core obstacle in automated formal proving. General-purpose LLMs write poor Lean4 proofs because there is almost no paired natural-language/formal (NL-FL) training data, and their pretraining mixes in outdated Lean3 code.
  2. MA-LoT: Model-Collaboration Lean-based Long Chain-of-Thought Reasoning enhances Formal Theorem Proving (2025 · 18 citations)
    The paper addresses automated theorem proving in Lean4. Current LLM approaches fall into two camps. Step-by-step tree search uses verifier feedback but is costly and lacks high-level planning. Whole-proof generation exploits natural-language reasoning but never sees intermediate verifier signals. MA-LoT bridges these…

 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 55 times (of the 149,439 papers whose bibliographies we read); 17 citing papers with a page here →.

Updated

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