Elamur Research⌕
☰
home / authors / Terufumi Morishita

Terufumi Morishita

Terufumi Morishita has 4 papers in our library, in 2022–2026, most of them on Language models & pretraining, Evaluation & benchmarks, Reasoning; often with Gaku Morio, Yasuhiro Sogawa, Yuta Koreeda.

4 papers in our library · 2022–2026

OpenAlex · ORCID · Wikidata · h-index 6 · 44 works · 154 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 20 times in our library · ⭐ Paper Stars ranking: #9131

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

🏢HitachiTokyo · Japan2019–2026 · 35 paperscurrent

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).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksPhilosophy · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksEvaluation & benchmarks · 1 workReasoning · 1 work

 Works by year

2020s

20263 papers20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. End-to-end Argument Mining with Cross-corpora Multi-task Learning (2022 · 43 citations)
    Argument mining (AM) recovers the structure of an argument from text. It finds argumentative spans, assigns them types such as Claim or Premise, and links them with labelled relations such as Support or Attack. Annotated AM data is small and costly to produce. Existing corpora also follow incompatible schemes for…
  2. Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates (2026 · 3 citations)
    Instruct LLMs perform poorly in languages that are rare in their training data, and adapting them is difficult.
  3. Disentangling the Effects of Unlearning in Measuring Parametric Faithfulness of Chain-of-Thought (2026)
    Tutek et al. (2025) proposed parametric faithfulness, which tests whether a Chain-of-Thought (CoT) reflects a model's internal reasoning. The method unlearns individual CoT steps and checks whether the model's No-CoT answer changes. That design attributes every answer change to the removed reasoning step. This paper…
  4. Is Micro Domain-Adaptive Pre-Training Effective for Real-World Operations? Multi-Step Evaluation Reveals Potential and Bottlenecks (2026)
    Enterprises need LLMs that can work with narrow, proprietary knowledge tied to specific operations ("micro domains").

 Co-authors here

9 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 20 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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