Bansal, Mohit

LXMERT: Learning Cross-Modality Encoder Representations from Transformers

Tan, H., & Bansal, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 2,319

Adversarial NLI: A New Benchmark for Natural Language Understanding

Nie, Y., Williams, A., Dinan, E., Bansal, M., Weston, J., & Kiela, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 656

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

TVQA: Localized, Compositional Video Question Answering

Lei, J., Yu, L., Bansal, M., & Berg, T. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Multimodal Machine Learning ApplicationsVideo Analysis and Summarization

Cited by 541

VL-ADAPTER: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks

Sung, Y.-L., Cho, J., & Bansal, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 329

Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout

Tan, H., Yu, L., & Bansal, M. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 293

Dynabench: Rethinking Benchmarking in NLP

Kiela, D., Bartolo, M., Nie, Y., Kaushik, D., Geiger, A., Wu, Z., Vidgen, B., Prasad, G., Singh, A., Ringshia, P., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 254

Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?

Hase, P., & Bansal, M. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 254

MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning

Lei, J., Wang, L., Shen, Y., Yu, D., Berg, T., & Bansal, M. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Multimodal Machine Learning ApplicationsVideo Analysis and Summarization

Cited by 179

Revealing the Importance of Semantic Retrieval for Machine Reading at Scale

Nie, Y., Wang, S., & Bansal, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingInformation Retrieval and Search Behavior

Cited by 132

Envedit: Environment Editing for Vision-and-Language Navigation

Li, J., Tan, H., & Bansal, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

Cited by 87

GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models

Prasad, A., Hase, P., Zhou, X., & Bansal, M. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 78

ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs

Chen, J. C.-Y., Saha, S., & Bansal, M. (2023) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 64

Scaling Data Generation in Vision-and-Language Navigation

Wang, Z., Li, J., Hong, Y., Wang, Y., Wu, Q., Bansal, M., Gould, S., Tan, H., & Qiao, Y. (2023) · arXiv

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

Cited by 64

ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs

Chen, J., Saha, S., & Bansal, M. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingMultimodal Machine Learning Applications

Cited by 64

Scaling Data Generation in Vision-and-Language Navigation

Wang, Z., Li, J., Hong, Y., Wang, Y., Wu, Q., Bansal, M., Gould, S., Tan, H., & Qiao, Y. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

Cited by 64

Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?

Hase, P., Zhang, S., Xie, H., & Bansal, M. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 62

InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection

Fung, Y., Thomas, C., Gangi Reddy, R., Polisetty, S., Ji, H., Chang, S.-F., McKeown, K., Bansal, M., & Sil, A. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Misinformation and Its ImpactsTopic Modeling

Cited by 52

Diagnosing the Environment Bias in Vision-and-Language Navigation

Zhang, Y., Tan, H., & Bansal, M. (2020) · Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

Cited by 51

GTBench: Uncovering the Strategic Reasoning Capabilities of LLMs via Game-Theoretic Evaluations

Duan, J., Zhang, R., Diffenderfer, J., Kailkhura, B., Sun, L., Stengel-Eskin, E., Bansal, M., Chen, T., & Xu, K. (2024) · Advances in Neural Information Processing Systems 37

Artificial Intelligence in GamesGame Theory and Applications

Cited by 39

MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning

Chen, J., Prasad, A., Saha, S., Stengel-Eskin, E., & Bansal, M. (2025) · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 38

ManyModalQA: Modality Disambiguation and QA over Diverse Inputs

Hannan, D., Jain, A., & Bansal, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 37

VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning

Tan, H., Lei, J., Wolf, T., & Bansal, M. (2021) · arXiv

Multimodal Machine Learning ApplicationsHuman Pose and Action Recognition

Cited by 35

The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions

Zhou, X., Nie, Y., Tan, H., & Bansal, M. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 32