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Ansong Ni

Ansong Ni has 2 papers in our library, in 2023–2024, most of them on Code & programming languages, Language models & pretraining; often with Caiming Xiong, Dragomir Radev, Martin Riddell.

2 papers in our library · 2023–2024

OpenAlex · h-index 13 · 41 works · 647 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 551 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #6151

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

🎓Yale UniversityNew Haven · United States2021–2024 · 24 papers🎓Pennsylvania State UniversityState College · United States2021–2022 · 6 papers🔬Allen Institute for Artificial IntelligenceSeattle · United States2021–2023 · 3 papers🎓George Mason UniversityFairfax · United States2022 · 2 papers🎓Nanjing UniversityNanjing · China2017–2018 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksInformation Systems · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Code & programming languages · 2 worksLanguage models & pretraining · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. LEVER: Learning to Verify Language-to-Code Generation with Execution (2023 · 126 citations)
    Code-oriented large language models can turn natural-language requests into programs, but their single best guess is often wrong.
  2. L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models (2024 · 27 citations)
    Large language models trained on code can turn natural-language requests into executable programs, but earlier evaluations covered isolated tasks, single model families or particular learning setups.

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. LEVER: Learning to Verify Language-to-Code Generation with ExecutionarXiv:2302.08468Ansong Ni, Srini Iyer, Dragomir Radev, et al.2023 · 126 citations
  2. Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions ↗arXiv:2205.14318 · no summary yetAnsong Ni et al.2022 · 39 citations
  3. NExT: Teaching Large Language Models to Reason about Code Execution ↗arXiv:2404.14662 · no summary yetAnsong Ni et al.2024 · 30 citations
  4. L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language ModelsTransactions of the Association for Computational Linguistics (TACL)Ansong Ni, Pengcheng Yin, Yilun Zhao, et al.2024 · 27 citations
  5. Merging Weak and Active Supervision for Semantic Parsing ↗arXiv:1911.12986 · no summary yetAnsong Ni, Pengcheng Yin, Graham Neubig2019 · 21 citations

 Co-authors here

12 of 13 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 551 times (of the 149,439 papers whose bibliographies we read); 111 citing papers with a page here →.

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