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Shichun Liu

Shichun Liu has 2 papers in our library, in 2026, most of them on Language models & pretraining; often with Chengjun Xiao, Jingqi Tong, Junzhe Wang.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 12 · 36 works · 813 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 144 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #10313

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

🎓Fudan UniversityShanghai · China2024–2026 · 17 paperscurrent🎓China University of Petroleum, East ChinaQingdao · China2021–2023 · 8 papers🎓Northwest Normal UniversityLanzhou · China2018–2022 · 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 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 workSafety Research · 1 workSocial Sciences · 1 work

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

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding (2026 · 4 citations)
    Coding scaffolds such as Claude Code, Kilo and Droid turn LLMs into repository-level agents. Existing evaluations are either single-turn instruction-following tests or outcome-based agent benchmarks that check whether tests pass. Neither shows whether an agent respects rules that come from many sources and must be…
  2. LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models (2026 · 3 citations)
    Fixed public test sets can leak into training corpora and be overfitted, so leaderboard scores may overstate real LLM ability.

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. A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models ↗arXiv:2303.10420 · no summary yetJunjie Ye et al.2023 · 283 citations
  2. Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning ↗arXiv:2402.05808 · no summary yetZhiheng Xi et al.2024 · 36 citations
  3. OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic CodingAnnual Meeting of the Association for Computational Linguistics (ACL)Deming Ding, Shichun Liu, Enhui Yang, et al.2026 · 4 citations
  4. LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Ming Zhang, Yujiong Shen, Jingyi Deng, et al.2026 · 3 citations
  5. ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetJie Yang et al.2026

 Co-authors here

7 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 144 times (of the 149,439 papers whose bibliographies we read); 33 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/shichun-liu/