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Silviu Pitis

Silviu Pitis has 2 papers in our library, in 2022–2023, most of them on Language models & pretraining; often with Jimmy Lei Ba, Yongchao Zhou, Andrew Wang.

2 papers in our library · 2022–2023

OpenAlex · h-index 9 · 25 works · 440 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 546 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #3152

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

🎓University of MichiganAnn Arbor · United States2026 · 1 papercurrent🎓University of TorontoToronto · Canada2019–2025 · 13 papers🔬Vector InstituteToronto · Canada2020–2024 · 5 papers🎓Georgia Institute of TechnologyAtlanta · United States2017–2018 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 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).Computer Science · 2 worksArtificial Intelligence · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 workSocial Sciences · 1 workSafety Research · 1 workSoftware · 1 work

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

 Works by year

2020s

20231 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Large Language Models Are Human-Level Prompt Engineers (2022 · 701 citations)
    How well a large language model does a task depends heavily on the wording of its instruction, and good prompts are usually written by hand through trial and error.
  2. Identifying the Risks of LM Agents with an LM-Emulated Sandbox (2023 · 96 citations)
    LM agents that call external tools, such as banking APIs, terminals or smart-home devices, can cause serious harm when they misread a request.

All works

6 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. Large Language Models Are Human-Level Prompt EngineersarXiv:2211.01910Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, et al.2022 · 701 citations
  2. Identifying the Risks of LM Agents with an LM-Emulated SandboxarXiv:2309.15817Yangjun Ruan, Honghua Dong, Andrew Wang, et al.2023 · 96 citations
  3. Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning ↗arXiv (Cornell University) · no summary yetSilviu Pitis et al.2020 · 45 citations
  4. Boosted Prompt Ensembles for Large Language Models ↗arXiv (Cornell University) · no summary yetSilviu Pitis et al.2023 · 38 citations
  5. An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality ↗arXiv (Cornell University) · no summary yetSilviu Pitis et al.2020 · 4 citations
  6. Report Cards: Qualitative Evaluation of Language Models Using Natural Language Summaries ↗arXiv (Cornell University) · no summary yetBlair Yang et al.2024 · 1 citation

 Co-authors here

10 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 546 times (of the 149,439 papers whose bibliographies we read); 109 citing papers with a page here →.

Updated

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