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Michael Sullivan

Michael Sullivan has 2 papers in our library, in 2024–2026, most of them on Machine translation, Reasoning, Interpretability; often with Alexander Koller, Ji-Ung Lee.

2 papers in our library · 2024–2026

OpenAlex · h-index 14 · 102 works · 832 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 4 times in our library · ⭐ Paper Stars ranking: #10731

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

🎓Saarland UniversitySaarbrücken · Germany2025–2026 · 4 paperscurrent🎓University at Buffalo, State University of New YorkBuffalo · United States2003–2024 · 8 papers🎓University of MinnesotaMinneapolis · United States2016–2018 · 4 papers🎓Northwestern UniversityEvanston · United States1998–1999 · 3 papers🎓University of PittsburghPittsburgh · United States1997–1999 · 2 papers🏢Texas InstrumentsDallas · United States1989–1990 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 13 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 5The 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 work

🧭 Topics 3Research subjects and languages the work is about.Interpretability · 1 workReasoning · 1 workMachine translation · 1 work

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. It is not True that Transformers are Inductive Learners: Probing NLI Models with External Negation (2024 · 4 citations)
    The paper asks whether transformer models fine-tuned for natural language inference actually reason logically or only seem to.
  2. AuthorMix: Modular Authorship Style Transfer via Layer-wise Adapter Mixing (2026)
    AuthorMix trains one LoRA adapter for each of about ten high-resource authors. For a new low-resource target, it mixes these adapters with scalar weights, learned per layer with reinforcement learning (GRPO), using the joint style and meaning score of a few target examples as reward. The base model and adapters stay…

 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.

 Cited by

Papers of our library cite their works 4 times (of the 149,439 papers whose bibliographies we read).

Updated

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