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Pranav Shetty

Pranav Shetty has 2 papers in our library, in 2026, most of them on Language models & pretraining, Information extraction, Evaluation & benchmarks; often with Xiaomo Liu, Zhiqiang Ma, Manuela Veloso.

2 papers in our library · 2026

OpenAlex · h-index 0 · 8 works · 0 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1 time in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #12183

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

🏢JPMorgan Chase & CoNew York · United States2026 · 4 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 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Evaluation & benchmarks · 1 workInformation extraction · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Detecting Non-Membership in LLM Training Data via Rank Correlations (2026)
    Most auditing work on LLM training data asks whether a dataset was used (membership inference). This paper addresses the reverse question: how to show with statistical confidence that a model was not trained on a given dataset. Such evidence matters for copyright disputes, compliance checks, and agreements that…
  2. ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document Images (2026)
    Generalist vision-language models (VLMs) score well on standard document understanding benchmarks. It is less clear whether they can pull many related fields out of varied business documents and arrange them according to a schema the user defines on the fly. Existing resources do not test this well. KEE and RE…

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. Perturb Your Data: Paraphrase-Guided Training Data Watermarking ↗arXiv:2512.17075 · no summary yetPranav Shetty et al.2025 · 1 citation
  2. Detecting Non-Membership in LLM Training Data via Rank CorrelationsConference of the European Chapter of the Association for Computational Linguistics (EACL)Pranav Shetty, Mirazul Haque, Zhiqiang Ma, et al.2026
  3. ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document ImagesConference of the European Chapter of the Association for Computational Linguistics (EACL)Mathieu Sibue, Andrés Muñoz Garza, Samuel Mensah, et al.2026

 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 1 time (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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