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Agrima Seth

Agrima Seth has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining, Bias, fairness & safety, English; often with Kalika Bali, Harshita Diddee, Rishav Hada.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 3 · 6 works · 27 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 37 times in our library · ⭐ Paper Stars ranking: #7648

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 MichiganAnn Arbor · United States2023–2025 · 6 paperscurrent

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 9The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksSocial Sciences · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksSafety Research · 2 worksExperimental and Cognitive Psychology · 1 workPsychology · 1 workGender Studies · 1 work

🧭 Topics 3Research subjects and languages the work is about.Bias, fairness & safety · 1 workEnglish · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. DOSA: A Dataset of Social Artifacts from Different Indian Geographical Subcultures (2024 · 29 citations)
    LLMs learn mostly from web text, and the web underrepresents many communities. As a result, it is unclear how well these models know everyday cultural items from outside dominant cultures. The authors study this for state-level subcultures within India, arguing that culture varies inside a country and not only…
  2. “Fifty Shades of Bias”: Normative Ratings of Gender Bias in GPT Generated English Text (2023 · 27 citations)
    Most gender-bias resources label a sentence as simply biased or not. They are usually built from rigid templates or from lexicon-driven web mining, and both approaches tend to miss subtle, implicit bias. This paper treats bias as a matter of degree instead.

 Co-authors here

5 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 37 times (of the 149,439 papers whose bibliographies we read); 12 citing papers with a page here →.

Updated

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