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Ahmed Abbasi

Ahmed Abbasi has 2 papers in our library, in 2022–2026, most of them on Language models & pretraining, Bias, fairness & safety; often with Yi Yang.

2 papers in our library · 2022–2026

OpenAlex · ORCID · Wikidata · h-index 37 · 130 works · 6,142 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 78 times in our library · ⭐ Paper Stars ranking: #5772

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 Notre DameNotre Dame · United States2020–2026 · 54 papersEmploymentcurrent🎓University of VirginiaCharlottesville · United States2011–2020 · 38 papers🎓University of ArizonaTucson · United States2005–2013 · 15 papers🎓University of Wisconsin–MilwaukeeMilwaukee · United States2008–2013 · 11 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 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksSafety Research · 1 workSocial Sciences · 1 work

🧭 Topics 2Research subjects and languages the work is about.Bias, fairness & safety · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20261 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts (2022 · 191 citations)
    Large masked language models absorb social stereotypes from the text they are trained on. Earlier debiasing methods rely on external corpora, but those corpora may not cover the biases a model actually holds, and some even introduce new ones. Auto-Debias avoids external data by probing the model directly.
  2. Generalization or Memorization? Multi-Agent vs. Baseline LLMs and AutoML Models for Tabular Classification (2026)
    The paper asks whether LLMs that perform well on tabular classification are actually reasoning or simply recalling benchmarks seen during pre-training.

 Co-authors here

1 co-author 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); 5 citing papers with a page here →.

Updated

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