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Yoshi Suhara

Yoshi Suhara has 4 papers in our library, in 2024–2025, most of them on Language models & pretraining, Efficient models & compression; often with Ameya Sunil Mahabaleshwarkar, Shizhe Diao, Zijia Chen.

4 papers in our library · 2024–2025

OpenAlex · h-index 2 · 19 works · 9 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 351 times in our library · ⭐ Paper Stars ranking: #5720

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

🏢NvidiaReading · United Kingdom2024–2025 · 5 papers🏢NvidiaSanta Clara · United States2024–2025 · 5 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).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 2 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3 worksEfficient models & compression · 1 work

 Works by year

2020s

20251 paper20243 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Large Language Models are Inconsistent and Biased Evaluators (2024 · 91 citations)
    LLM-based judges such as GPT-4 are now widely used to score generated text without references. However, earlier work mostly tuned them to match human ratings and paid little attention to how reliable they are. The authors examine two properties of these judges, bias and consistency, in summary evaluation. They use…
  2. Hymba: A Hybrid-head Architecture for Small Language Models (2024 · 50 citations)
    Small language models must balance accuracy against memory and speed. Transformers recall context well, but their compute cost and KV cache grow quickly with sequence length. State space models such as Mamba run cheaply but recall poorly. Earlier hybrids stacked the two layer types one after another, so a weak layer…
  3. LLM Pruning and Distillation in Practice: The Minitron Approach (2024 · 44 citations)
    Minitron-style compression trains only the largest model in a family and derives smaller ones by structured pruning followed by knowledge distillation.
  4. When2Call: When (not) to Call Tools (2025 · 11 citations)
    Most tool-use benchmarks for language models test whether the right function is invoked with the right arguments. They rarely check whether a call should happen at all. When2Call targets this decision. Each item is framed as a four-way multiple-choice problem:

 Co-authors here

10 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 351 times (of the 149,439 papers whose bibliographies we read); 72 citing papers with a page here →.

Updated

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