Papers by Maitreya Patel
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks (2022.emnlp-main)
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Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Kuntal Kumar Pal, Maitreya Patel, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Savan Doshi, Shailaja Keyur Sampat, Siddhartha Mishra, Sujan Reddy A, Sumanta Patro, Tanay Dixit, Xudong Shen
| Challenge: | a benchmark of 1,616 diverse NLP tasks and their expert-written instructions is used to test generalization of models to unseen tasks . a recent study shows that instruction-following models outperform instruction-based models by over 9% . |
| Approach: | They build a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. |
| Outcome: | The proposed model outperforms existing instruction-following models by over 9% on the benchmark despite being smaller. |
Precision or Recall? An Analysis of Image Captions for Training Text-to-Image Generation Model (2024.findings-emnlp)
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| Challenge: | Recent advances in text-to-image models have demonstrated remarkable capabilities in image synthesis. |
| Approach: | They analyze the critical role of caption precision and recall in text-to-image model training. |
| Outcome: | The proposed model trains with synthetic captions that show similar behavior to those trained on human-annotated captions. |
CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question Answering (2022.emnlp-main)
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| Challenge: | Videos often capture objects, their visible properties, their motion, and the interactions between different objects. |
| Approach: | They propose a video question answering dataset for reasoning about the implicit physical properties of objects in a scene. |
| Outcome: | The proposed dataset enables evaluation under several out-of-distribution settings – videos with objects with masses, coefficients of friction, and initial velocities that are not observed in the training distribution. |
AcT2I: Evaluating and Improving Action Depiction in Text-to-Image Models (2025.emnlp-main)
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| Challenge: | Text-to-Image (T2I) models have been successful in generating images from textual descriptions, but they struggle to capture nuanced and implicit attributes inherent in action depiction. |
| Approach: | They propose a benchmark to evaluate the performance of T2I models in generating images from action-centric prompts. |
| Outcome: | The proposed model achieves an increase of 72% on AcT2I. |