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Gene Louis Kim

Gene Louis Kim has 2 papers in our library, in 2026, most of them on Language models & pretraining, Information extraction, Evaluation & benchmarks; often with Ninareh Mehrabi, Vasanth Sarathy.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 5 · 27 works · 65 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #11994

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 South FloridaTampa · United States2023–2026 · 15 paperscurrent🎓University of RochesterRochester · United States2016–2021 · 7 papers

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 worksComputer Vision and Pattern Recognition · 1 workSafety Research · 1 workSocial Sciences · 1 work

🧭 Topics 3Research subjects and languages the work is about.Evaluation & benchmarks · 1 workInformation extraction · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Breaking the Benchmark: Revealing LLM Bias via Minimal Contextual Augmentation (2026)
    Widely used fairness benchmarks are public and may have shaped LLM alignment. A model that scores well on them might therefore be fair only on the surface, having picked up benchmark-specific patterns rather than robust behavior. The authors test this with an automatic augmentation pipeline built from three swappable…
  2. Event Detection with a Context-Aware Encoder and LoRA for Improved Performance on Long-Tailed Classes (2026)
    The paper examines two persistent weaknesses in event detection (ED) research. First, decoder-only LLMs attend only to preceding tokens, which limits the token embeddings they produce for understanding tasks. Second, the field mostly reports Micro-F1, which rewards strong performance on frequent event types and hides…

 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

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