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Nitay Calderon

Nitay Calderon has 3 papers in our library, in 2025–2026, most of them on Language models & pretraining; often with Roi Reichart, Idan Szpektor, Orgad Keller.

3 papers in our library · 2025–2026

OpenAlex · h-index 6 · 45 works · 116 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 40 times in our library · ⭐ Paper Stars ranking: #7157

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

🎓Technion – Israel Institute of TechnologyHaifa · Israel2021–2026 · 22 paperscurrent🏢GoogleMountain View · United States2025–2026 · 2 paperscurrent🏢Applied Decision ScienceCincinnati · United States2026 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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 10The field of science this work belongs to (OpenAlex fields).Social Sciences · 2 worksDecision Sciences · 1 workArtificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 workLaw · 1 workPolitical Science and International Relations · 1 workStatistics, Probability and Uncertainty · 1 workSafety Research · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 2 works

 Works by year

2020s

20261 paper20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs (2025 · 52 citations)
    Researchers increasingly use LLMs instead of people to label data or evaluate model outputs. There is no accepted, statistically grounded way to decide whether this substitution is justified. Common checks such as F1, kappa or correlation have no clear pass/fail threshold and cannot show that an LLM beats a human.…
  2. Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance (2025 · 27 citations)
    Benchmark labels are often treated as ground truth, yet both expert and crowd annotation introduce mistakes.
  3. LIBERTy: A Causal Framework for Benchmarking Concept-Based Explanations of LLMs with Structural Counterfactuals (2026)
    Judging whether a concept-based explanation of an LLM is faithful requires the true causal effect of a concept, such as gender or experience, on the model's output.

 Co-authors here

4 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 40 times (of the 149,439 papers whose bibliographies we read); 11 citing papers with a page here →.

Updated

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