Elamur Research⌕
☰
home / authors / Michael Boratko

Michael Boratko

Michael Boratko has 2 papers in our library, in 2024, most of them on Language models & pretraining, Information retrieval & search; often with Jinhyuk Lee, Yi Luan, Zhuyun Dai.

2 papers in our library · 2024

OpenAlex · h-index 8 · 46 works · 337 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 186 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6477

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 Massachusetts AmherstAmherst Center · United States2018–2024 · 21 papers🎓The University of Texas at AustinAustin · United States2021 · 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksInformation Systems · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksInformation retrieval & search · 1 work

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Gecko: Versatile Text Embeddings Distilled from Large Language Models (2024 · 74 citations)
    General-purpose text embedding models need large, varied training data to work across many tasks, and human-labeled collections are costly and limited in coverage.
  2. Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? (2024 · 53 citations)
    Long-context language models (LCLMs) can now read inputs of up to a million tokens. This raises the question of whether they could replace separate tools such as retrievers, RAG pipelines and SQL engines. Existing long-context tests are either synthetic, like needle-in-a-haystack, or fixed in length, so they cannot…

All works

3 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. Modeling Fine-Grained Entity Types with Box Embeddings ↗Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yetYasumasa Onoe et al.2021 · 90 citations
  2. Gecko: Versatile Text Embeddings Distilled from Large Language ModelsarXiv:2403.20327Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren, et al.2024 · 74 citations
  3. Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?arXiv:2406.13121Jinhyuk Lee, Anthony Chen, Zhuyun Dai, et al.2024 · 53 citations

 Co-authors here

12 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 186 times (of the 149,439 papers whose bibliographies we read); 39 citing papers with a page here →.

Updated

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