| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2680 | 2023 |
| 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 | 1688 | 2022 |
| 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 | 1293* | 2022 |
| 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 | 796 | 2022 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 750 | 2024 |
| Cross-task generalization via natural language crowdsourcing instructions S Mishra, D Khashabi, C Baral, H Hajishirzi ACL, 2021 | 663 | 2021 |
| Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks Y Wang*, S Mishra*, P Alipoormolabashi, Y Kordi, A Mirzaei, A Naik, ... EMNLP, 2022 | 504 | 2022 |
| Benchmarking generalization via in-context instructions on 1,600+ language tasks Y Wang, S Mishra, P Alipoormolabashi, Y Kordi, A Mirzaei, A Arunkumar, ... arXiv preprint arXiv:2204.07705 2, 2022 | 324* | 2022 |
| Large language models cannot self-correct reasoning yet J Huang, X Chen, S Mishra, HS Zheng, AW Yu, X Song, D Zhou ICLR, 2023 | 247 | 2023 |
| Reframing Instructional Prompts to GPTk's Language S Mishra, D Khashabi, C Baral, Y Choi, H Hajishirzi ACL, 2021 | 201 | 2021 |
| Instruction-following evaluation for large language models J Zhou, T Lu, S Mishra, S Brahma, S Basu, Y Luan, D Zhou, L Hou arXiv preprint arXiv:2311.07911, 2023 | 137 | 2023 |
| Lila: A Unified Benchmark for Mathematical Reasoning S Mishra, M Finlayson, P Lu, L Tang, S Welleck, C Baral, T Rajpurohit, ... EMNLP, 2022 | 109 | 2022 |
| 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 | 98* | 2022 |
| Commonsense Reasoning with Implicit Knowledge in Natural Language P Banerjee*, S Mishra*, KK Pal*, A Mitra, C Baral AKBC, 2021 | 82* | 2021 |
| In-BoXBART: Get Instructions into Biomedical Multi-Task Learning M Parmar, S Mishra, M Purohit, M Luo, MH Murad, C Baral NAACL, 2022 | 65 | 2022 |
| Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions M Parmar*, S Mishra*, M Geva, C Baral EACL Outstanding Paper Award, 2022 | 63 | 2022 |
| Take a step back: evoking reasoning via abstraction in large language models HS Zheng, S Mishra, X Chen, HT Cheng, EH Chi, QV Le, D Zhou ICLR, 2023 | 61 | 2023 |
| Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings N Varshney, S Mishra, C Baral ACL, 2022 | 48 | 2022 |
| How FaR Are Large Language Models From Agents with Theory-of-Mind? P Zhou, A Madaan, SP Potharaju, A Gupta, KR McKee, A Holtzman, ... arXiv preprint arXiv:2310.03051, 2023 | 46 | 2023 |
| Instructabsa: Instruction learning for aspect based sentiment analysis K Scaria, H Gupta, S Goyal, SA Sawant, S Mishra, C Baral NAACL, 2023 | 42 | 2023 |
Chitta BaralProfessor of Computer Science, Arizona State UniversityVerified email at asu.edu
