Elamur Research⌕
☰
home / authors / Jake M. Hofman

Jake M. Hofman

Jake M. Hofman has 2 papers in our library, in 2023–2025, most of them on Evaluation & benchmarks; often with Ashton Anderson, Sayash Kapoor, Serina Y. Chang.

2 papers in our library · 2023–2025

OpenAlex · ORCID · Wikidata · h-index 30 · 120 works · 9,016 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 38 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #10580

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

🏢Microsoft Research New York CityNew York · United States2013–2026 · 29 paperscurrent🏢MicrosoftRedmond · United States2016–2026 · 22 paperscurrent🎓Columbia UniversityNew York · United States2006–2009 · 8 papers🏢YahooSunnyvale · United States2009–2021 · 7 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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 8The field of science this work belongs to (OpenAlex fields).Decision Sciences · 1 workInformation Systems and Management · 1 workSafety Research · 1 workSocial Sciences · 1 workArtificial Intelligence · 1 workArts and Humanities · 1 workComputer Science · 1 workLanguage and Linguistics · 1 work

🧭 Topics 1Research subjects and languages the work is about.Evaluation & benchmarks · 1 work

 Works by year

2020s

20251 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. REFORMS: Reporting Standards for Machine Learning Based Science (2023 · 19 citations)
    Machine learning is spreading quickly across scientific disciplines, and so are problems with validity, reproducibility, and generalizability.
  2. ChatBench: From Static Benchmarks to Human-AI Evaluation (2025 · 9 citations)
    Benchmarks such as MMLU test LLMs in isolation, but people use chatbots through open-ended dialogue. This paper asks whether "AI-alone" scores reflect how well a person does when working with a model. The authors turned MMLU questions into conversations. Prolific workers received a question, answered it on their own…

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. Integrating explanation and prediction in computational social science ↗Nature · no summary yetJake M. Hofman et al.2021 · 370 citations
  2. Scalable recommendation with hierarchical Poisson factorization ↗no summary yetPrem Gopalan, Jake M. Hofman, David M. Blei2015 · 199 citations
  3. Scalable Recommendation with Poisson Factorization ↗arXiv (Cornell University) · no summary yetPrem Gopalan, Jake M. Hofman, David M. Blei2013 · 111 citations
  4. REFORMS: Reporting Standards for Machine Learning Based SciencearXiv:2308.07832Sayash Kapoor, Emily Cantrell, Kenny Peng, et al.2023 · 19 citations
  5. ChatBench: From Static Benchmarks to Human-AI EvaluationAnnual Meeting of the Association for Computational Linguistics (ACL)Serina Chang, Ashton Anderson, Jake M. Hofman2025 · 9 citations
  6. Scalable recommendation with Poisson factorization,” arXiv:1311.1704 ↗no summary yetPrem Gopalan, Jake M. Hofman, David M. Blei2013 · 1 citation

 Co-authors here

3 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 38 times (of the 149,439 papers whose bibliographies we read); 4 citing papers with a page here →.

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/jake-m-hofman/