| Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks Y Wang, S Mishra, P Alipoormolabashi, Y Kordi, A Mirzaei, A Arunkumar, ... EMNLP 2022, 2022 | 839* | 2022 |
| In-boxbart: Get instructions into biomedical multi-task learning M Parmar, S Mishra, M Purohit, M Luo, MH Murad, C Baral NAACL 2022 Findings, 2022 | 66 | 2022 |
| Residual neural network precisely quantifies dysarthria severity-level based on short-duration speech segments S Gupta, AT Patil, M Purohit, M Parmar, M Patel, HA Patil, RC Guido Neural Networks 139, 105-117, 2021 | 65 | 2021 |
| Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions M Parmar, S Mishra, M Geva, C Baral EACL 2023 (Outstanding Paper Award), 2023 | 62 | 2023 |
| Is a Question Decomposition Unit All We Need? P Patel, S Mishra, M Parmar, C Baral EMNLP 2022, 2022 | 41 | 2022 |
| Effectiveness of cross-domain architectures for whisper-to-normal speech conversion M Parmar, S Doshi, NJ Shah, M Patel, HA Patil 2019 27th European Signal Processing Conference (EUSIPCO), 1-5, 2019 | 26 | 2019 |
| LogicBench: Towards systematic evaluation of logical reasoning ability of large language models M Parmar, N Patel, N Varshney, M Nakamura, M Luo, S Mashetty, A Mitra, ... ACL 2024 (Main), 13679-13707, 2024 | 23* | 2024 |
| Novel MMSE DiscoGAN for cross-domain whisper-to-speech conversion NJ Shah, M Parmar, N Shah, HA Patil Machine Learning in Speech and Language Processing (MLSLP) Workshop, 1-3, 2018 | 21 | 2018 |
| Novel Adaptive Generative Adversarial Network for Voice Conversion M Patel, M Parmar, S Doshi, N Shah, HA Patil Asia-Pacific Signal and Information Processing Association (APSIPA), 1273-1281, 2019 | 20* | 2019 |
| Towards logiglue: A brief survey and a benchmark for analyzing logical reasoning capabilities of language models M Luo, S Kumbhar, M Parmar, N Varshney, P Banerjee, S Aditya, C Baral arXiv preprint arXiv:2310.00836, 2023 | 19 | 2023 |
| Less is more: Summary of long instructions is better for program synthesis K Kuznia, S Mishra, M Parmar, C Baral EMNLP 2022, 2022 | 19 | 2022 |
| Investigating Acceleration of LLaMA Inference by Enabling Intermediate Layer Decoding via Instruction Tuning with ‘LITE’ N Varshney, A Chatterjee, M Parmar, C Baral Findings of the Association for Computational Linguistics: NAACL 2024, 3656-3677, 2024 | 18* | 2024 |
| TarGEN: Targeted data generation with large language models H Gupta, K Scaria, U Anantheswaran, S Verma, M Parmar, SA Sawant, ... COLM 2024, 2023 | 18 | 2023 |
| How Many Data Samples is an Additional Instruction Worth? RS Puri, S Mishra, M Parmar, C Baral EACL 2023 Findings, 2023 | 17 | 2023 |
| Intelligibility improvement of dysarthric speech using mmse discogan M Purohit, M Patel, H Malaviya, A Patil, M Parmar, N Shah, S Doshi, ... 2020 International Conference on Signal Processing and Communications (SPCOM …, 2020 | 16 | 2020 |
| Biotabqa: Instruction learning for biomedical table question answering M Luo, S Saxena, S Mishra, M Parmar, C Baral arXiv preprint arXiv:2207.02419, 2022 | 14 | 2022 |
| Novel Inception-GAN for Whisper-to-Normal speech conversion M Patel, M Parmar, S Doshi, N Shah, HA Patil Proceedings of 10th ISCA Speech Synthesis Workshop (SSW 10), Vienna, Austria …, 2019 | 13 | 2019 |
| Logicattack: Adversarial attacks for evaluating logical consistency of natural language inference M Nakamura*, S Mashetty*, M Parmar*, N Varshney, C Baral Findings of the Association for Computational Linguistics: EMNLP 2023, 13322 …, 2023 | 7 | 2023 |
| Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models N Patel*, M Kulkarni*, M Parmar*, A Budhiraja, M Nakamura, N Varshney, ... EMNLP 2024 Main, 2024 | 6 | 2024 |
| Weak speech supervision: A case study of dysarthria severity classification M Purohit, M Parmar, M Patel, H Malaviya, HA Patii 2020 28th European Signal Processing Conference (EUSIPCO), 101-105, 2021 | 6 | 2021 |
Chitta BaralProfessor of Computer Science, Arizona State UniversityEmail verificata su asu.edu