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David Stap

David Stap has 2 papers in our library, in 2025–2026, most of them on Multilingual & cross-lingual NLP, Machine translation, Evaluation & benchmarks; often with Genta Indra Winata, Jay Gala, Kenneth Christian Enevoldsen.

2 papers in our library · 2025–2026

OpenAlex · h-index 5 · 32 works · 395 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 320 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5076

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

🎓University of AmsterdamAmsterdam · Netherlands2020–2026 · 24 paperscurrent🏢NXAI GmbHLinz · Austria2025–2026 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 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 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 2 worksEvaluation & benchmarks · 1 workMachine translation · 1 work

 Works by year

2020s

20261 paperAnalyzing and Improving Cross-lingual Knowledge Transfer for Machine Translation20251 paperMMTEB: Massive Multilingual Text Embedding Benchmark

 Papers

The most cited first (OpenAlex and our library).

  1. MMTEB: Massive Multilingual Text Embedding Benchmark (2025 · 101 citations)
    Embedding models are usually tested on narrow benchmarks that cover few languages, domains and task types, and full evaluation is often too costly for communities with little compute.
  2. Analyzing and Improving Cross-lingual Knowledge Transfer for Machine Translation (2026)
    Most of the world's languages have little parallel data, so translating them depends on multilingual models reusing knowledge learned from better-resourced languages. This EAMT 2026 thesis abstract by David Stap (University of Amsterdam) studies that cross-lingual transfer from four angles.

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. MMTEB: Massive Multilingual Text Embedding BenchmarkarXiv:2502.13595Kenneth Enevoldsen, Isaac Chung, Imene Kerboua, et al.2025 · 101 citations
  2. Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?ICLR 2025 (arXiv:2409.19151) · original ↗ · no summary yetSeth Aycock et al.2025 · 31 citations
  3. The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM AbilitiesProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · original ↗ · no summary yetDavid Stap et al.2024 · 22 citations
  4. Analyzing and Improving Cross-lingual Knowledge Transfer for Machine TranslationAnnual Conference of the European Association for Machine Translation (EAMT)David Stap2026

 Co-authors here

4 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 320 times (of the 141,840 papers whose bibliographies we read); 29 citing papers with a page here →.

Updated

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