🧑‍🔬 Eric Zelikman

🏛️ Affiliations

🎓Stanford UniversityStanford · United States2020–2025 · 13 papersEmploymentcurrent

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.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. STaR: Bootstrapping Reasoning With Reasoning (2022 · 777 citations)
   STaR (Self-Taught Reasoner) is a self-training loop for teaching a language model to produce step-by-step rationales.
2. Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels (2024 · 16 citations)
   Teaching a language model to respect a set of behavioral rules (a "constitution") usually depends on human preference labels, curated demonstrations, or reward models with reinforcement learning.

All works

12 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. Holistic Evaluation of Language Models ↗arXiv:2211.09110 · Published in Transactions on Machine Learning Research (TMLR), 2023 · no summary yetPercy Liang et al.2022 · 1,001 citations
2. STaR: Bootstrapping Reasoning With ReasoningEric Zelikman, Yuhuai Wu, Jesse Mu, et al.Mova's core list · 777 citations
3. Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions ↗arXiv:2212.10561 · no summary yetEric Zelikman et al.2023 · 56 citations
4. Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metrics ↗Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · no summary yetElisa Kreiss et al.2022 · 32 citations
5. Certified Deductive Reasoning with Language Models ↗arXiv:2306.04031 · no summary yetGabriel Poesia et al.2023 · 23 citations
6. Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference LabelsConference on Neural Information Processing Systems (NeurIPS)Jan-Philipp Franken, Eric Zelikman, Rafael Rafailov, et al.2024 · 16 citations
7. Generating and Evaluating Tests for K-12 Students with Language Model Simulations: A Case Study on Sentence Reading Efficiency ↗Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yetEric Zelikman et al.2023 · 16 citations
8. Evaluating the Disentanglement of Deep Generative Models through Manifold Topology ↗arXiv (Cornell University) · no summary yetSharon Zhou et al.2020 · 8 citations
9. ContextRef: Evaluating Referenceless Metrics For Image Description Generation ↗arXiv (Cornell University) · no summary yetElisa Kreiss et al.2023 · 3 citations
10. Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared Randomness ↗arXiv (Cornell University) · no summary yetEric Zelikman et al.2023 · 3 citations
11. Lexinvariant Language Models ↗neural information processing systems · no summary yetQian Huang et al.2023 · 2 citations
12. Contextual Salience for Fast and Accurate Sentence Vectors ↗arXiv (Cornell University) · no summary yetEric Zelikman, Richard Socher2018 · 1 citation

🤝 Co-authors here

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

• Noah D. Goodman ×2
• Jesse Mu
• Kanishk Gandhi
• Rafael Rafailov
• Tobias Gerstenberg
• Yuhuai Wu

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Conference on Neural Information Processing Systems (NeurIPS) ×2: NeurIPS 2024, NeurIPS 2022
• Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing ×2
• neural information processing systems

🔗 Cited by

Papers of our library cite their works 1,597 times (of the 149,439 papers whose bibliographies we read); 372 citing papers with a page here →.

Updated 30 October 2026

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