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Mehar Bhatia

Mehar Bhatia has 2 papers in our library, in 2025, most of them on Language models & pretraining, Evaluation & benchmarks, Reasoning; often with Arkil Patel, Benno Krojer, Bill Yuchen Lin.

2 papers in our library · 2025

OpenAlex · ORCID · h-index 5 · 17 works · 50 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 78 times in our library · ⭐ Paper Stars ranking: #8752

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

🎓McGill UniversityMontreal · Canada2024–2026 · 6 paperscurrent🔬Mila - Quebec Artificial Intelligence InstituteMontreal · Canada2025–2026 · 5 paperscurrent🎓University of British ColumbiaVancouver · Canada2023–2024 · 5 papers🔬Vector InstituteToronto · Canada2023–2024 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 1 workBias, fairness & safety · 1 workEvaluation & benchmarks · 1 work

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning (2025 · 47 citations)
    Large Reasoning Models (LRMs) produce long reasoning chains before giving an answer. Their internal behaviour has been hard to study because OpenAI's o1 hides its thoughts. DeepSeek-R1 exposes its reasoning, so the authors use it to launch a research programme they call "Thoughtology."
  2. CulturalBench: A Robust, Diverse and Challenging Benchmark for Measuring LMs' Cultural Knowledge Through Human-AI Red-Teaming (2025 · 38 citations)
    Measuring how well language models understand everyday cultural practice worldwide is difficult. Static hand-written test sets soon become too easy. Benchmarks built from web pages, value surveys or synthetic generation may overlap with training data or carry over model biases.

 Co-authors here

12 of 19 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 78 times (of the 149,439 papers whose bibliographies we read); 30 citing papers with a page here →.

Updated

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