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Megh Thakkar

Megh Thakkar has 2 papers in our library, in 2022–2024, most of them on Language models & pretraining, Summarization, Evaluation & benchmarks; often with Ahmed Masry, Enamul Hoque, Shafiq Joty.

2 papers in our library · 2022–2024

OpenAlex · ORCID · h-index 6 · 27 works · 171 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 111 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #6798

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

🎓Université de MontréalMontreal · Canada2024–2025 · 5 paperscurrent🎓École de Technologie SupérieureMontreal · Canada2025 · 1 papercurrent🎓Birla Institute of Technology and Science, PilaniPilāni · India2021–2023 · 9 papers🔬Mila - Quebec Artificial Intelligence InstituteMontreal · Canada2022–2025 · 8 papers🎓Georgia Institute of TechnologyAtlanta · United States2021–2022 · 3 papers🎓Nanyang Technological UniversitySingapore · Singapore2022–2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 7 more institutions 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 · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 5Research subjects and languages the work is about.Evaluation & benchmarks · 1 workSummarization · 1 workLanguage models & pretraining · 1 workReasoning · 1 workVision & language · 1 work

 Works by year

2020s

20241 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Chart-to-Text: A Large-Scale Benchmark for Chart Summarization (2022 · 163 citations)
    The paper addresses automatic chart summarization: producing natural-language text that explains the main takeaways of a chart.
  2. ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild (2024 · 32 citations)
    Most chart-understanding models learn from instruction data built from a chart's underlying data table. That approach discards visual cues such as trends, annotations and layout. These models also often use vision-language backbones whose image and text parts are only loosely aligned. ChartGemma addresses both…

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. Chart-to-Text: A Large-Scale Benchmark for Chart SummarizationAnnual Meeting of the Association for Computational Linguistics (ACL)Shankar Kantharaj, Rixie Tiffany Leong, Xiang Lin, et al.2022 · 163 citations
  2. ChartGemma: Visual Instruction-tuning for Chart Reasoning in the WildarXiv:2407.04172Ahmed Masry, Megh Thakkar, Aayush Bajaj, et al.2024 · 32 citations
  3. HypMix: Hyperbolic Interpolative Data Augmentation ↗Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · no summary yetRamit Sawhney et al.2021 · 10 citations
  4. DMix: Adaptive Distance-aware Interpolative Mixup ↗Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · no summary yetRamit Sawhney et al.2022 · 9 citations

 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 111 times (of the 149,439 papers whose bibliographies we read); 28 citing papers with a page here →.

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