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Hugh Leather

Hugh Leather has 2 papers in our library, in 2024, most of them on Language models & pretraining, Evaluation & benchmarks, Reasoning; often with Baptiste Rozière, Gabriel Synnaeve, Alex Gu.

2 papers in our library · 2024

OpenAlex · ORCID · Wikidata · h-index 22 · 94 works · 1,764 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 129 times in our library · ⭐ Paper Stars ranking: #7661

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 EdinburghEdinburgh · United Kingdom2007–2025 · 39 paperscurrent🏢MetaMenlo Park · United States2020–2025 · 18 paperscurrent🎓University of California San DiegoSan Diego · United States2023 · 2 papers🏢MetaLondon · United Kingdom2020–2022 · 2 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.

 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 worksArts and Humanities · 1 workArtificial Intelligence · 2 worksInformation Systems · 2 worksComputer Science Applications · 1 workLanguage and Linguistics · 1 workHardware and Architecture · 1 work

🧭 Topics 3Research subjects and languages the work is about.Evaluation & benchmarks · 1 workReasoning · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution (2024 · 122 citations)
    Code language models are usually judged on HumanEval or MBPP. Those benchmarks test whether a model can write short programs from a natural-language description, but they do not test whether it understands what a piece of code does when it runs. CRUXEval fills that gap. It has 800 short Python functions, each paired…
  2. Meta Large Language Model Compiler: Foundation Models of Compiler Optimization (2024 · 16 citations)
    General-purpose code LLMs see very little assembly or compiler IR during training, so they handle optimization poorly and often produce broken code.

 Co-authors here

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

 Cited by

Papers of our library cite their works 129 times (of the 149,439 papers whose bibliographies we read); 46 citing papers with a page here →.

Updated

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