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Mubashara Akhtar

Mubashara Akhtar has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning, Interpretability; often with Artem Shelmanov, Chenxi Pang, Ekaterina Fadeeva.

3 papers in our library · 2023–2026

OpenAlex · ORCID · Wikidata · h-index 8 · 17 works · 107 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 29 times in our library · ⭐ Paper Stars ranking: #9135

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

🎓ETH ZurichZurich · Switzerland2025–2026 · 5 paperscurrent🎓King's College LondonLondon · United Kingdom2022–2025 · 8 papersEmployment

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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 3 worksLanguage and Linguistics · 3 worksInformation Systems · 1 work

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

 Works by year

2020s

20261 paper20251 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data (2023 · 38 citations)
    The authors investigate how well language models handle numbers and where exactly they make mistakes. Most previous benchmarks test only isolated aspects of numerical skills. The authors therefore propose a hierarchical taxonomy of eleven types of numerical reasoning. It has four levels, ranging from recognizing how…
  2. TANQ: An Open Domain Dataset of Table Answered Questions (2025 · 7 citations)
    Most question-answering benchmarks for LLMs expect a short string answer drawn from a single document or table.
  3. ReProbe: Efficient Test-Time Scaling of Multi-Step Reasoning by Probing Internal States of Large Language Models (2026 · 1 citation)
    Test-time scaling improves LLM reasoning by sampling several candidate reasoning steps or full chains and keeping the most promising ones.

 Co-authors here

12 of 15 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 29 times (of the 149,439 papers whose bibliographies we read); 8 citing papers with a page here →.

Updated

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