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Kazutoshi Shinoda

Kazutoshi Shinoda has 2 papers in our library, in 2026, most of them on Language models & pretraining, Bias, fairness & safety; often with Kyosuke Nishida, Hiroaki Sugiyama, Kosuke Nishida.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 5 · 24 works · 127 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 28 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #12080

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

🏢NTTTokyo · Japan2019–2026 · 17 paperscurrent🎓The University of TokyoTokyo · Japan2013–2023 · 8 papersEducation 2013–2017 · Bachelor of Engineering, Department of EngineeringEducation 2017–2019 · Master of Information Science and Technology, Department of Computer ScienceEducation since 2019 · Ph.D. in Information Science and Technology, Department of Computer Science🔬National Institute of InformaticsTokyo · Japan2021–2023 · 3 papers
🏛️NTT CorporationKanagawa · Japan2023Employment since 2023 · Researcher, NTT Human Informatics Laboratories

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 9The 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 workDevelopmental and Educational Psychology · 1 workPsychology · 1 workSafety Research · 1 workSocial Sciences · 1 work

🧭 Topics 2Research subjects and languages the work is about.Bias, fairness & safety · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Debiasing Reward Models via Causally Motivated Inference-Time Intervention (2026)
    Reward models (RMs) used in RLHF often reward surface style instead of substance. They favor longer answers, more paragraphs, exclamation marks, bold Markdown and lexical overlap with the query, and these preferences can carry over into aligned LLMs. Earlier inference-time fixes handle only length and tend to trade…
  2. Let's Put Ourselves in Sally's Shoes: Shoes-of-Others Prefilling Improves Theory of Mind in Large Language Models (2026)
    Large language models still trail humans on Theory of Mind (ToM), and fine-tuning on ToM data tends to hurt out-of-distribution generalization.

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. Multi-style Generative Reading Comprehension ↗Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · no summary yetKyosuke Nishida et al.2019 · 98 citations
  2. Debiasing Reward Models via Causally Motivated Inference-Time InterventionAnnual Meeting of the Association for Computational Linguistics (ACL)Kazutoshi Shinoda, Kosuke Nishida, Kyosuke Nishida2026
  3. Let's Put Ourselves in Sally's Shoes: Shoes-of-Others Prefilling Improves Theory of Mind in Large Language ModelsFindings of the Association for Computational LinguisticsKazutoshi Shinoda, Nobukatsu Hojo, Kyosuke Nishida, et al.2026

 Co-authors here

4 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 28 times (of the 149,439 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated

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