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Ken Yano

Ken Yano has 2 papers in our library, in 2026, most of them on Language models & pretraining, Named entity recognition; often with Hiroya Takamura.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 4 · 24 works · 43 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #13709

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

🔬National Institute of Advanced Industrial Science and TechnologyTsukuba · Japan2023–2026 · 8 paperscurrent🔬Artificial Intelligence Research CenterTokyo · Japan2023–2026 · 3 paperscurrent🎓Nara Institute of Science and TechnologyIkoma · Japan2017–2018 · 4 papers🎓University of Electro-CommunicationsTokyo · Japan2021–2023 · 2 papers🔬Advanced Telecommunications Research Institute InternationalKyoto · Japan2015–2016 · 2 papers🎓Fukuoka UniversityFukuoka · Japan2012 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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 2Research subjects and languages the work is about.Language models & pretraining · 2 worksNamed entity recognition · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Assessing the Belief Consistency of Large Language Models on the Logical Conversation Process (2026)
    The paper asks whether an LLM's hidden preference stays logically coherent as a conversation grows. This matters if generated reasoning is to be read as a faithful trace of the model's state. Existing consistency benchmarks rely on questions with correct answers, so task skill confounds the measurement.
  2. Treating Decoder-Only LLMs as Encoders: A Simple and Effective Fine-tuning Approach for Named Entity Recognition (2026)
    Decoder-only LLMs are trained with causal masking, so each token's representation sees only what comes before it.

 Co-authors here

1 co-author 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/ken-yano/