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Mark Ibrahim

Mark Ibrahim has 2 papers in our library, in 2023–2025, most of them on Language models & pretraining, Reasoning, Evaluation & benchmarks; often with Dieuwke Hupkes, Emmanuel Dupoux, Gregoire Mialon.

2 papers in our library · 2023–2025

OpenAlex · ORCID · h-index 9 · 37 works · 446 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 47 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #9705

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

🏢MetaMenlo Park · United States2021–2023 · 8 papers🏢MetaLondon · United Kingdom2021 · 4 papers🏢Capital OneTysons Corner · United States2018–2020 · 4 papers

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.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 2 worksEvaluation & benchmarks · 1 work

 Works by year

2020s

20251 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions (2025 · 35 citations)
    Large language models often receive questions that have no reliable answer. A request may omit key details, rest on a false assumption, depend on opinion, or concern facts that are outdated or unknown. The authors argue that declining or hedging in these cases matters as much as accuracy, but no broad evaluation of…
  2. WorldSense: A Synthetic Benchmark for Grounded Reasoning in Large Language Models (2023 · 15 citations)
    LLMs often seem to understand the physical world, but it is hard to tell whether they build coherent internal scene representations.

All works

3 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. AbstentionBench: Reasoning LLMs Fail on Unanswerable QuestionsConference on Neural Information Processing Systems (NeurIPS)Polina Kirichenko, Mark Ibrahim, Kamalika Chaudhuri, et al.2025 · 35 citations
  2. WorldSense: A Synthetic Benchmark for Grounded Reasoning in Large Language ModelsarXiv:2311.15930Youssef Benchekroun, Megi Dervishi, Mark Ibrahim, et al.2023 · 15 citations
  3. X-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs ↗arXiv (Cornell University) · no summary yetVlad Sobal et al.2024

 Co-authors here

8 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 47 times (of the 149,439 papers whose bibliographies we read); 15 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/mark-ibrahim/