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Peter Hase

Peter Hase has 3 papers in our library, in 2020–2023, most of them on Language models & pretraining, Evaluation & benchmarks, Interpretability; often with Mohit Bansal, Archiki Prasad, Shiyue Zhang.

3 papers in our library · 2020–2023

OpenAlex · h-index 10 · 34 works · 724 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 403 citations in our library · 5 more in our library without a page yet · ⭐ Paper Star rank #3027

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 North Carolina at Chapel HillChapel Hill · United States2018–2026 · 24 paperscurrent🏢Philip Morris InternationalNew York · United States2026 · 1 papercurrent🤝Schmidt SciencesNew York · United States2026 · 1 papercurrent🎓Stanford UniversityStanford · United States2026 · 1 papercurrent🏢AnthropicSan Francisco2024–2025 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 7 Computer Science · 3 works Artificial Intelligence · 3 works Arts and Humanities · 2 works Language and Linguistics · 2 works Social Sciences · 1 work Safety Research · 1 work Software · 1 work

🧭 Topics 3 Language models & pretraining · 1 work Evaluation & benchmarks · 1 work Interpretability · 1 work

 Works by year

2020s

20231 paper20202 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? (2020 · 309 citations)
    Many algorithms claim to make machine learning models interpretable, but there is little evidence that they help people anticipate what a model will do.
  2. GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models (2023 · 182 citations)
    Instructions written for human annotators often work poorly as prompts for large language models. Manual rewriting is slow and subjective, and gradient-based prompt tuning is expensive and impossible for models available only through an API. The authors propose GrIPS (Gradient-free Instructional Prompt Search). It…
  3. Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? (2020 · 120 citations)
    Models trained on datasets that pair labels with free-text human explanations can write their own natural language justifications.

Also in our library

5 more works of theirs are in our library without a page here yet; they will appear as we publish them.

 Co-authors here

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

 Cited by

Papers of our library cite their works 403 times (of the 141,840 papers whose bibliographies we read); 27 citing papers with a page here →.

Updated

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