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Kenji Kawaguchi

Kenji Kawaguchi has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining, Evaluation & benchmarks, Reasoning; often with Yiran Zhao, Junxian He, Lidong Bing.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 41 · 412 works · 8,679 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 278 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #4454

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 University of SingaporeSingapore · Singapore2021–2026 · 90 paperscurrent🎓Harvard UniversityCambridge · United States2019–2023 · 18 papers🎓Massachusetts Institute of TechnologyCambridge · United States2016–2021 · 18 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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).Computer Science · 2 worksArtificial Intelligence · 2 worksComputer Networks and Communications · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workEvaluation & benchmarks · 1 workReasoning · 1 work

 Works by year

2020s

20241 paperKenji Kawaguchi's papers of 202420231 paperKenji Kawaguchi's papers of 2023

 Papers

The most cited first (OpenAlex and our library).

  1. Self-Evaluation Guided Beam Search for Reasoning (2023 · 145 citations)
    Large language models reason better when they write out intermediate steps, but in long chains small mistakes pile up, and the number of possible chains grows exponentially.
  2. How do Large Language Models Handle Multilingualism? (2024 · 98 citations)
    This paper investigates how large language models internally process non-English input. The authors decoded each layer's hidden states into vocabulary tokens for Vicuna-13b-v1.5 and BLOOMZ-7b1. Non-English queries shifted toward English-dominated representations in the middle layers and returned to the query language…

All works

5 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. Self-Evaluation Guided Beam Search for ReasoningConference on Neural Information Processing Systems (NeurIPS)Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, et al.2023 · 145 citations
  2. How do Large Language Models Handle Multilingualism?Conference on Neural Information Processing Systems (NeurIPS)Yiran Zhao, Wenxuan Zhang, Guizhen Chen, et al.2024 · 98 citations
  3. Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks ↗arXiv:2305.18395 · no summary yetMinki Kang et al.2023 · 71 citations
  4. LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs ↗Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · no summary yetDo Xuan Long et al.2025 · 14 citations
  5. Aligning Large Language Models with Human Opinions through Persona Selection and Value — Belief — Norm Reasoning ↗arXiv:2311.08385 · no summary yetDo Xuan Long et al.2023 · 12 citations

 Co-authors here

7 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 278 times (of the 141,840 papers whose bibliographies we read); 17 citing papers with a page here →.

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