| Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks Y Wang, S Mishra, P Alipoormolabashi, Y Kordi, A Mirzaei, A Arunkumar, ... EMNLP 2022 2204, 2022 | 517* | 2022 |
| Benchmarking generalization via in-context instructions on 1,600+ language tasks Y Wang, S Mishra, P Alipoormolabashi, Y Kordi, A Mirzaei, A Arunkumar, ... EMNLP 2022, 2022 | 118 | 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, 2022 | 72 | 2022 |
| A stitch in time saves nine: Detecting and mitigating hallucinations of llms by validating low-confidence generation N Varshney, W Yao, H Zhang, J Chen, D Yu arXiv preprint arXiv:2307.03987, 2023 | 69 | 2023 |
| Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings N Varshney, S Mishra, C Baral ACL 2022 Findings, 2022 | 44 | 2022 |
| Towards Improving Selective Prediction Ability of NLP Systems N Varshney, S Mishra, C Baral ACL 2022 RepL4NLP, 2022 | 21 | 2022 |
| " John is 50 years old, can his son be 65?" Evaluating NLP Models' Understanding of Feasibility H Gupta, N Varshney, S Mishra, KK Pal, SA Sawant, K Scaria, S Goyal, ... EACL 2023, 2023 | 17 | 2023 |
| ILDAE: Instance-Level Difficulty Analysis of Evaluation Data N Varshney, S Mishra, C Baral ACL 2022, 2022 | 17 | 2022 |
| Model Cascading: Towards Jointly Improving Efficiency and Accuracy of NLP Systems N Varshney, C Baral EMNLP 2022, 2022 | 14 | 2022 |
| Towards question format independent numerical reasoning: A set of prerequisite tasks S Mishra, A Mitra, N Varshney, B Sachdeva, C Baral ACL 2022, 2022 | 13 | 2022 |
| Post-Abstention: Towards Reliably Re-Attempting the Abstained Instances in QA N Varshney, C Baral ACL 2023, 2023 | 12 | 2023 |
| Unsupervised Natural Language Inference Using PHL Triplet Generation N Varshney, P Banerjee, T Gokhale, C Baral ACL 2022 Findings, 2022 | 11 | 2022 |
| Accelerating LLaMA Inference by Enabling Intermediate Layer Decoding via Instruction Tuning with LITE N Varshney, A Chatterjee, M Parmar, C Baral NAACL 2024 Findings, 2023 | 10* | 2023 |
| The Art of Defending: A Systematic Evaluation and Analysis of LLM Defense Strategies on Safety and Over-Defensiveness N Varshney, P Dolin, A Seth, C Baral ACL 2024 Findings, 2023 | 10 | 2023 |
| Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans? N Varshney, M Luo, C Baral AAAI'23 Workshop on Knowledge Augmented Methods for NLP, 2023 | 10 | 2023 |
| It's better to say" I can't answer" than answering incorrectly: Towards Safety critical NLP systems N Varshney, S Mishra, C Baral ACL 2022 RepL4NLP, 2022 | 9 | 2022 |
| 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 | 8 | 2023 |
| Can Transformers Reason About Effects of Actions? P Banerjee, C Baral, M Luo, A Mitra, K Pal, TC Son, N Varshney arXiv preprint arXiv:2012.09938, 2020 | 8 | 2020 |
| Can NLP Models Correctly Reason Over Contexts that Break the Common Assumptions? N Varshney, M Parmar, N Patel, D Handa, S Sarkar, M Luo, C Baral arXiv preprint arXiv:2305.12096, 2023 | 4 | 2023 |
| Let the Model Decide its Curriculum for Multitask Learning N Varshney, S Mishra, C Baral NAACL 2022 Deep Learning for Low-Resource NLP, 2022 | 4 | 2022 |
Chitta BaralProfessor of Computer Science, Arizona State University在 asu.edu 的电子邮件经过验证
