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Kosuke Nishida

Kosuke Nishida has 2 papers in our library, in 2026, most of them on Language models & pretraining, Summarization, Evaluation & benchmarks; often with Kyosuke Nishida, Kazutoshi Shinoda.

2 papers in our library · 2026

OpenAlex · h-index 7 · 28 works · 269 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 64 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #12088

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 · Japan2017–2026 · 28 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 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 4Research subjects and languages the work is about.Bias, fairness & safety · 1 workEvaluation & benchmarks · 1 workLanguage models & pretraining · 1 workSummarization · 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. InstructSum: A Benchmark to Evaluate Instruction-Following Capability of Large Language Models in Summarization (2026)
    LLMs tuned to satisfy users often pad their answers with unrequested material. This is a serious flaw in summarization, where brevity is the point, and existing instruction-following benchmarks rarely address it. The authors present InstructSum, an English benchmark built from Wikipedia. Article abstracts serve as…

All works

4 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. Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction ↗Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · no summary yetKosuke Nishida et al.2019 · 127 citations
  2. 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
  3. Debiasing Reward Models via Causally Motivated Inference-Time InterventionAnnual Meeting of the Association for Computational Linguistics (ACL)Kazutoshi Shinoda, Kosuke Nishida, Kyosuke Nishida2026
  4. InstructSum: A Benchmark to Evaluate Instruction-Following Capability of Large Language Models in SummarizationLanguage Resources and Evaluation Conference (LREC)Kosuke Nishida, Kyosuke Nishida, Itsumi Saito2026

 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 64 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated

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