| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... TMLR, 2022 | 362* | 2022 |
| Cross-task generalization via natural language crowdsourcing instructions S Mishra, D Khashabi, C Baral, H Hajishirzi ACL, 2021 | 249* | 2021 |
| Self-Instruct: Aligning Language Model with Self Generated Instructions Y Wang, Y Kordi, S Mishra, A Liu, NA Smith, D Khashabi, H Hajishirzi ACL, 2022 | 246 | 2022 |
| Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks Y Wang*, S Mishra*, P Alipoormolabashi, Y Kordi, A Mirzaei, A Naik, ... EMNLP, 2022 | 210* | 2022 |
| Reframing Instructional Prompts to GPTk's Language S Mishra, D Khashabi, C Baral, Y Choi, H Hajishirzi ACL, 2021 | 91 | 2021 |
| Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering P Lu, S Mishra, T Xia, L Qiu, KW Chang, SC Zhu, O Tafjord, P Clark, ... NeurIPS, 2022 | 88 | 2022 |
| Commonsense Reasoning with Implicit Knowledge in Natural Language P Banerjee*, S Mishra*, KK Pal*, A Mitra, C Baral AKBC, 2021 | 69* | 2021 |
| NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks S Mishra, A Mitra, N Varshney, B Sachdeva, P Clark, C Baral, A Kalyan ACL, 2022 | 42* | 2022 |
| In-BoXBART: Get Instructions into Biomedical Multi-Task Learning M Parmar, S Mishra, M Purohit, M Luo, MH Murad, C Baral NAACL, 2022 | 29 | 2022 |
| Lila: A Unified Benchmark for Mathematical Reasoning S Mishra, M Finlayson, P Lu, L Tang, S Welleck, C Baral, T Rajpurohit, ... EMNLP, 2022 | 28 | 2022 |
| Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings N Varshney, S Mishra, C Baral ACL, 2022 | 26 | 2022 |
| Dqi: Measuring data quality in nlp S Mishra, A Arunkumar, B Sachdeva, C Bryan, C Baral ICML UDL, 2020 | 26* | 2020 |
| How Robust are Model Rankings: A Leaderboard Customization Approach for Equitable Evaluation S Mishra, A Arunkumar AAAI, 2021 | 24 | 2021 |
| Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions M Parmar*, S Mishra*, M Geva, C Baral EACL, 2022 | 23 | 2022 |
| Do we need to create big datasets to learn a task? S Mishra, BS Sachdeva SustaiNLP, 2020 | 22 | 2020 |
| Handling multi-parametric variations in distributed control of cyber-physical energy systems through optimal communication design MP Korukonda, SR Mishra, A Shukla, L Behera IET Cyber-Physical Systems Journal, 2017 | 22 | 2017 |
| Hardness of Samples Need to be Quantified for a Reliable Evaluation System: Exploring Potential Opportunities with a New Task S Mishra, A Arunkumar, C Bryan, C Baral ICML UDL, 2022 | 20* | 2022 |
| Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness T Gokhale*, S Mishra*, M Luo, BS Sachdeva, C Baral ACL, 2022 | 20 | 2022 |
| It's better to say" I can't answer" than answering incorrectly: Towards Safety critical NLP systems N Varshney, S Mishra, C Baral Repl4NLP 2022, 2020 | 19* | 2020 |
| Hybrid adaptive framework for coordinated control of distributed generators in cyber-physical energy systems MP Korukonda, SR Mishra, K Rajawat, L Behera IET Cyber-Physical Systems Journal, 2018 | 18 | 2018 |
Chitta BaralProfessor of Computer Science, Arizona State UniversityVerified email at asu.edu
