Hajishirzi, Hannaneh

Self-Instruct: Aligning Language Models with Self-Generated Instructions

Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., & Hajishirzi, H. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 1,243

ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

Mehta, S., Rastegari, M., Caspi, A., Shapiro, L., & Hajishirzi, H. (2018) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 1,029

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 755

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 752

Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

Luan, Y., He, L., Ostendorf, M., & Hajishirzi, H. (2018) · arXiv

Topic ModelingBiomedical Text Mining and Ontologies

Cited by 714

Entity, Relation, and Event Extraction with Contextualized Span Representations

Wadden, D., Wennberg, U., Luan, Y., & Hajishirzi, H. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 612

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

ESPNetv2: A Light-Weight, Power Efficient, and General Purpose Convolutional Neural Network

Mehta, S., Rastegari, M., Shapiro, L., & Hajishirzi, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 551

UNIFIEDQA: Crossing Format Boundaries with a Single QA System

Khashabi, D., Min, S., Khot, T., Sabharwal, A., Tafjord, O., Clark, P., & Hajishirzi, H. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020

Topic ModelingMultimodal Machine Learning Applications

Cited by 514

Robust fine-tuning of zero-shot models

Wortsman, M., Ilharco, G., Kim, J. W., Li, M., Kornblith, S., Roelofs, R., Lopes, R. G., Hajishirzi, H., Farhadi, A., Namkoong, H., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 428

Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Dodge, J., Ilharco, G., Schwartz, R., Farhadi, A., Hajishirzi, H., & Smith, N. (2020) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 412

FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-T., Koh, P., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 407

Fact or Fiction: Verifying Scientific Claims

Wadden, D., Lin, S., Lo, K., Wang, L. L., van Zuylen, M., Cohan, A., & Hajishirzi, H. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Misinformation and Its ImpactsTopic Modeling

Cited by 382

When not to trust language models: Investigating effectiveness of parametric and non-parametric memories

Mallen, A., Asai, A., Zhong, V., Das, R., Khashabi, D., & Hajishirzi, H. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 340

A Diagram is Worth a Dozen Images

Kembhavi, A., Salvato, M., Kolve, E., Seo, M., Hajishirzi, H., & Farhadi, A. (2016) · Lecture Notes in Computer Science

Multimodal Machine Learning ApplicationsHandwritten Text Recognition Techniques

Cited by 290

Cross-Task Generalization via Natural Language Crowdsourcing Instructions

Mishra, S., Khashabi, D., Baral, C., & Hajishirzi, H. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 269

Cross-Task Generalization via Natural Language Crowdsourcing Instructions

Mishra, S., Khashabi, D., Baral, C., & Hajishirzi, H. (2021) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 269

Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension

Kembhavi, A., Seo, M., Schwenk, D., Choi, J., Farhadi, A., & Hajishirzi, H. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 244

Multi-hop Reading Comprehension through Question Decomposition and Rescoring

Min, S., Zhong, V., Zettlemoyer, L., & Hajishirzi, H. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingMultimodal Machine Learning Applications

Cited by 197

MetaICL: Learning to Learn In Context

Min, S., Lewis, M., Zettlemoyer, L., & Hajishirzi, H. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 169

SciREX: A Challenge Dataset for Document-Level Information Extraction

Jain, S., van Zuylen, M., Hajishirzi, H., & Beltagy, I. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingBiomedical Text Mining and Ontologies

Cited by 159

Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering

Asai, A., Hashimoto, K., Hajishirzi, H., Socher, R., & Xiong, C. (2019) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 152

Compositional Questions Do Not Necessitate Multi-hop Reasoning

Min, S., Wallace, E., Singh, S., Gardner, M., Hajishirzi, H., & Zettlemoyer, L. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingInformation Retrieval and Search Behavior

Cited by 138

DataComp: In search of the next generation of multimodal datasets

Gadre, S. Y., Ilharco, G., Fang, A., Hayase, J., Smyrnis, G., Nguyen, T., Marten, R., Wortsman, M., Ghosh, D., Zhang, J., et al. (2023) · Advances in Neural Information Processing Systems 36

Multimodal Machine Learning ApplicationsImage Retrieval and Classification Techniques

Cited by 120