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Parker Seegmiller

Parker Seegmiller has 2 papers in our library, in 2026, most of them on Language models & pretraining, Evaluation & benchmarks; often with Sarah Masud Preum, Joseph Gatto.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 3 · 36 works · 28 citations OpenAlex, 2026-10-14

📄 2 works in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #12176

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

🎓Dartmouth CollegeHanover · United States2022–2026 · 23 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 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksHealth Informatics · 1 workMedicine · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksEvaluation & benchmarks · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. How Much Would a Clinician Edit This Draft? Evaluating LLM Alignment for Patient Message Response Drafting (2026)
    Clinicians are increasingly asked to review LLM-generated drafts of replies to patient portal messages. The authors ask whether such drafts actually reduce their workload or just shift effort into editing. With 13 primary care physicians and nurses, they built a taxonomy of what good clinician replies contain: 8…
  2. Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance (2026)
    Deployed LLMs face user prompts that change over time as new tasks and new users appear. Earlier robustness studies usually assumed out-of-distribution shift rather than measuring it. This paper asks how much prompt drift occurs in practice and how it affects instruction-tuned models. The authors present LENS, a…

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. How Much Would a Clinician Edit This Draft? Evaluating LLM Alignment for Patient Message Response DraftingAnnual Meeting of the Association for Computational Linguistics (ACL)Parker Seegmiller, Joseph Gatto, Sarah E. Greer, et al.2026
  2. Measuring Distribution Shift in User Prompts and Its Effects on LLM PerformanceAnnual Meeting of the Association for Computational Linguistics (ACL)Parker Seegmiller, Sarah Masud Preum2026
  3. FLAMES: Improving LLM Math Reasoning via a Fine-Grained Analysis of the Data Synthesis Pipeline ↗Findings of the Association for Computational Linguistics: EMNLP 2025 · no summary yetParker Seegmiller et al.2025

 Co-authors here

2 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.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/parker-seegmiller/