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Tanuja Ganu

Tanuja Ganu has 3 papers in our library, in 2023–2026, most of them on Vision & language, Language models & pretraining, Multilingual & cross-lingual NLP; often with Akshay Nambi, Haizhou Shi, Hao Wang.

3 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 15 · 83 works · 713 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 311 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4914

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

🏢Microsoft ResearchBengaluru · India2013–2026 · 43 paperscurrent🏢MicrosoftRedmond · United States2021–2026 · 5 paperscurrent🏥Narayana HealthBengaluru · India2026 · 1 papercurrent🏢IBMBengaluru · India2012–2015 · 12 papers🔬IBM Research - IndiaNew Delhi · India2012–2014 · 8 papers🎓Carnegie Mellon UniversityPittsburgh · United States2023 · 2 papers🎓University of WashingtonSeattle · United States2023 · 2 papers🏢MicrosoftHyderabad · India2022 · 2 papers🏢IBMArmonk · United States2014–2015 · 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 · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 4Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 1 workVision & language · 2 worksEvaluation & benchmarks · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20261 paper20251 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. MEGA: Multilingual Evaluation of Generative AI (2023 · 285 citations)
    Most evaluations of generative large language models have focused on English, so it is unclear how well they handle other languages.
  2. Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models (2025 · 21 citations)
    Evaluations of multimodal large language models (MLLMs) usually involve one or a few images, or rely on too few test cases to be statistically meaningful.
  3. Mind's Eye: A Benchmark of Visual Abstraction, Transformation and Composition for Multimodal LLMs (2026)
    Multimodal LLMs perform well on standard vision-language benchmarks. It is less clear whether they can carry out the mental operations humans use on visual puzzles, such as rotating, folding or recombining shapes. Existing benchmarks also often allow models to lean on language priors instead of the image.

All works

4 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. MEGA: Multilingual Evaluation of Generative AIarXiv:2303.12528Kabir Ahuja, Harshita Diddee, Rishav Hada, et al.2023 · 285 citations
  2. Evaluating LLMs' Mathematical Reasoning in Financial Document Question Answering ↗Findings of the Association for Computational Linguistics: ACL 2024 · no summary yetPragya Srivastava et al.2024 · 48 citations
  3. Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language ModelsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Hengyi Wang, Haizhou Shi, Shiwei Tan, et al.2025 · 21 citations
  4. Mind's Eye: A Benchmark of Visual Abstraction, Transformation and Composition for Multimodal LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Rohit Sinha, Aditya Sanjiv Kanade, Sai Srinivas Kancheti, et al.2026

 Co-authors here

11 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 311 times (of the 149,439 papers whose bibliographies we read); 72 citing papers with a page here →.

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