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Liangchen Luo

Liangchen Luo has 3 papers in our library, in 2023–2024, most of them on Language models & pretraining, Reasoning; often with Jingbo Shang, Lei Meng, Lei Shu.

3 papers in our library · 2023–2024

OpenAlex · h-index 7 · 18 works · 403 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 256 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #6469

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

🏢GoogleMountain View · United States2020–2024 · 4 paperscurrent🎓University of California San DiegoSan Diego · United States2023–2024 · 2 paperscurrent🎓Peking UniversityBeijing · China2018–2019 · 6 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.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksApplied Mathematics · 1 workMathematics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 2 worksLanguage models & pretraining · 2 works

 Works by year

2020s

20242 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Improve Mathematical Reasoning in Language Models by Automated Process Supervision (2024 · 168 citations)
    Process Reward Models (PRMs) score each intermediate step of a solution. On multi-step math they help LLMs more than verifiers that judge only the final answer. The catch is the step-level labels PRMs need. Until now these came from paid human annotators or from brute-force Monte Carlo rollouts launched at every…
  2. Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision (2024 · 43 citations)
    Training verifiers that score each intermediate step of a solution (process supervision) helps LLMs solve multi-step math and coding problems.
  3. Critique Ability of Large Language Models (2023 · 12 citations)
    LLMs are increasingly asked to judge outputs, yet there was no standard way to measure how well they detect mistakes in answers.

All works

5 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Improve Mathematical Reasoning in Language Models by Automated Process SupervisionarXiv:2406.06592Liangchen Luo, Yinxiao Liu, Rosanne Liu, et al.2024 · 168 citations
  2. RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting ↗arXiv:2305.15685 · AAAI 2024 · no summary yetLei Shu et al.2023 · 70 citations
  3. Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process SupervisionFindings of the Association for Computational LinguisticsZihan Wang, Yunxuan Li, Yuexin Wu, et al.2024 · 43 citations
  4. Critique Ability of Large Language ModelsarXiv:2310.04815Liangchen Luo, Zi Lin, Yinxiao Liu, et al.2023 · 12 citations
  5. Towards an On-device Agent for Text Rewriting ↗Findings of the Association for Computational Linguistics: NAACL 2024 · no summary yetYun Zhu et al.2024 · 8 citations

 Co-authors here

12 of 15 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 256 times (of the 149,439 papers whose bibliographies we read); 96 citing papers with a page here →.

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