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Ronak Pradeep

Ronak Pradeep has 3 papers in our library, in 2020–2023, most of them on Language models & pretraining, Information retrieval & search; often with Jimmy Lin, Rodrigo Nogueira, Sahel Sharifymoghaddam.

3 papers in our library · 2020–2023

OpenAlex · ORCID · Wikidata · h-index 16 · 60 works · 1,385 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 662 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #3346

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 WaterlooWaterloo · Canada2020–2026 · 50 paperscurrent

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

 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 · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksInformation Systems · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 2Research subjects and languages the work is about.Information retrieval & search · 2 worksLanguage models & pretraining · 2 works

 Works by year

2020s

20232 papers20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Document Ranking with a Pretrained Sequence-to-Sequence Model (2020 · 543 citations)
    The paper asks whether an encoder–decoder model can rank documents better than the usual approach, where an encoder-only model such as BERT is fine-tuned as a relevance classifier.
  2. Zero-Shot Listwise Document Reranking with a Large Language Model (2023 · 105 citations)
    Strong neural rerankers usually depend on large collections of human relevance labels such as MS MARCO. The authors ask whether a competitive ranking pipeline can be built without any task-specific training. They propose LRL (Listwise Reranker with a Large Language Model). It prompts GPT-3 with a query and up to ten…
  3. RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models (2023 · 81 citations)
    LLM-based listwise reranking has so far relied on proprietary models such as GPT 3.5 and GPT 4 accessed through APIs.

All works

5 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Document Ranking with a Pretrained Sequence-to-Sequence ModelFindings of the Association for Computational LinguisticsRodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, et al.2020 · 543 citations
  2. Pyserini: A Python Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations ↗International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · no summary yetJimmy Lin et al.2021 · 542 citations
  3. Zero-Shot Listwise Document Reranking with a Large Language ModelarXiv:2305.02156Xueguang Ma, Xinyu Zhang, Ronak Pradeep, et al.2023 · 105 citations
  4. RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language ModelsarXiv:2309.15088Ronak Pradeep, Sahel Sharifymoghaddam, Jimmy Lin2023 · 81 citations
  5. Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset ↗Proceedings of the First Workshop on Scholarly Document Processing · no summary yetEdwin Zhang et al.2020 · 66 citations

 Co-authors here

6 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 662 times (of the 149,439 papers whose bibliographies we read); 146 citing papers with a page here →.

Updated

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