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Victor Ruhle

Victor Ruhle has 2 papers in our library, in 2024, most of them on Language models & pretraining, Efficient models & compression; often with Ahmed Hassan Awadallah, Chi Wang, Chin-Yew Lin.

2 papers in our library · 2024

OpenAlex · ORCID · h-index 16 · 61 works · 1,891 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 201 times in our library · ⭐ Paper Stars ranking: #5901

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

🏢MicrosoftRedmond · United States2021–2026 · 12 paperscurrent🔬Max Planck Institute for Polymer ResearchMainz · Germany2008–2012 · 9 papers🎓University of CambridgeCambridge · United Kingdom2012–2015 · 5 papers🏢Microsoft ResearchCambridge · United Kingdom2020–2025 · 3 papers🏢MicrosoftReading · United Kingdom2022–2023 · 2 papers🔬Max Planck SocietyMunich · Germany2008–2011 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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 · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

🧭 Topics 2Research subjects and languages the work is about.Efficient models & compression · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression (2024 · 161 citations)
    Long prompts built with techniques like RAG, in-context learning or chain-of-thought increase the cost and latency of LLM inference.
  2. Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing (2024 · 98 citations)
    Large LLMs answer well but need costly cloud hardware. Smaller models are cheap enough for edge devices but usually give weaker answers. The authors note that many queries are "easy," and a small model handles them about as well as a big one. They propose hybrid inference: a learned router sends each query to either…

 Co-authors here

12 of 15 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 201 times (of the 149,439 papers whose bibliographies we read); 54 citing papers with a page here →.

Updated

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