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Cassie S. Mitchell

Cassie S. Mitchell has 2 papers in our library, in 2026, most of them on Language models & pretraining, Named entity recognition, Summarization.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 23 · 83 works · 1,464 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #13464

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

🎓Georgia Institute of TechnologyAtlanta · United States2004–2026 · 83 papersEducation 2004–2009Employment 2011–2017Employment since 2017current🎓Emory UniversityAtlanta · United States2007–2026 · 59 papersEducationEmploymentcurrent🎓The Wallace H. Coulter Department of Biomedical EngineeringAtlanta · United States2007–2026 · 7 paperscurrent🏛️Emory University School of MedicineAtlanta · United States2009–2010Education 2009–2010🎓Oklahoma State UniversityStillwater · United States2000–2004Education 2000–2004

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 7The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksBiochemistry, Genetics and Molecular Biology · 1 workComputer Vision and Pattern Recognition · 1 workMolecular Biology · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksNamed entity recognition · 1 workSummarization · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Understanding LLMs' summarization capabilities: an analysis of biomedical abstract and lay summary generation (2026)
    The paper asks how well small, deployable LLMs (at most 8B parameters) can write two kinds of biomedical summaries without fine-tuning: technical abstracts for researchers and plain-language lay summaries for non-experts.
  2. Where do LLMs currently stand on biomedical NER in both clean and noisy settings ? (2026)
    This paper revisits how well current large language models handle biomedical named entity recognition, a task long dominated by domain-adapted BERT encoders.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

Updated

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