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. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 118

X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers

Cho, J., Lu, J., Schwenk, D., Hajishirzi, H., & Kembhavi, A. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 99

s1: Simple test-time scaling

Muennighoff, N., Yang, Z., Shi, W., Li, X. L., Fei-Fei, L., Hajishirzi, H., Zettlemoyer, L., Liang, P., Candes, E., & Hashimoto, T. (2025) · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 76

OLMo: Accelerating the Science of Language Models

Groeneveld, D., Beltagy, I., Walsh, E., Bhagia, A., Kinney, R., Tafjord, O., Jha, A., Ivison, H., Magnusson, I., Wang, Y., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 70

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Deitke, M., Clark, C., Lee, S., Tripathi, R., Yang, Y., Park, J. S., Salehi, M., Muennighoff, N., Lo, K., Soldaini, L., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 67

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Deitke, M., Clark, C., Lee, S., Tripathi, R., Yang, Y., Park, J. S., Salehi, M., Muennighoff, N., Lo, K., Soldaini, L., et al. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 67

Editing Models with Task Arithmetic

Ilharco, G., Ribeiro, M. T., Wortsman, M., Gururangan, S., Schmidt, L., Hajishirzi, H., & Farhadi, A. (2022) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 66

MetaICL: Learning to Learn In Context

Min, S., Lewis, M., Zettlemoyer, L., & Hajishirzi, H. (2021) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 61

MultiVerS: Improving scientific claim verification with weak supervision and full-document context

Wadden, D., Lo, K., Wang, L., Cohan, A., Beltagy, I., & Hajishirzi, H. (2022) · Findings of the Association for Computational Linguistics: NAACL 2022

Misinformation and Its ImpactsTopic Modeling

Cited by 60

Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization

Ramamurthy, R., Ammanabrolu, P., Brantley, K., Hessel, J., Sifa, R., Bauckhage, C., Hajishirzi, H., & Choi, Y. (2022) · arXiv

Reinforcement Learning in RoboticsNatural Language Processing Techniques

Cited by 56

Dolma: an open corpus of three trillion tokens for language model pretraining research

Soldaini, L., Kinney, R., Bhagia, A., Schwenk, D., Atkinson, D., Authur, R., Bogin, B., Chandu, K., Dumas, J., Elazar, Y., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 56

Fine-grained Hallucination Detection and Editing for Language Models

Mishra, A., Asai, A., Balachandran, V., Wang, Y., Neubig, G., Tsvetkov, Y., & Hajishirzi, H. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 49

Task-aware Retrieval with Instructions

Asai, A., Schick, T., Lewis, P., Chen, X., Izacard, G., Riedel, S., Hajishirzi, H., & Yih, W.-T. (2023) · Findings of the Association for Computational Linguistics: ACL 2023

Information Retrieval and Search BehaviorTopic Modeling

Cited by 46

Fine-Grained Human Feedback Gives Better Rewards for Language Model Training

Wu, Z., Hu, Y., Shi, W., Dziri, N., Suhr, A., Ammanabrolu, P., Smith, N. A., Ostendorf, M., & Hajishirzi, H. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 40

Synthesizing scientific literature with retrieval-augmented language models

Asai, A., He, J., Shao, R., Shi, W., Singh, A., Chang, J. C., Lo, K., Soldaini, L., Feldman, S., D’Arcy, M., et al. (2026) · Nature

Topic ModelingInformation Retrieval and Search Behavior

Cited by 38

SciFact-Open: Towards open-domain scientific claim verification

Wadden, D., Lo, K., Kuehl, B., Cohan, A., Beltagy, I., Wang, L. L., & Hajishirzi, H. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022

Topic ModelingMisinformation and Its Impacts

Cited by 35

SciFact-Open: Towards open-domain scientific claim verification

Wadden, D., Lo, K., Kuehl, B., Cohan, A., Beltagy, I., Wang, L. L., & Hajishirzi, H. (2022) · arXiv

Topic ModelingMisinformation and Its Impacts

Cited by 35

A General Framework for Information Extraction using Dynamic Span Graphs

Luan, Y., Wadden, D., He, L., Shah, A., Ostendorf, M., & Hajishirzi, H. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 34

“A theme-rewriting approach for generating algebra word problems,”

Koncel-Kedziorski, R., Konstas, I., Zettlemoyer, L., & Hajishirzi, H. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 30

Do Membership Inference Attacks Work on Large Language Models?

Duan, M., Suri, A., Mireshghallah, N., Min, S., Shi, W., Zettlemoyer, L., Tsvetkov, Y., Choi, Y., Evans, D., & Hajishirzi, H. (2024) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 17

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. W., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 14

RewardBench: Evaluating Reward Models for Language Modeling

Lambert, N., Pyatkin, V., Morrison, J., Miranda, L., Lin, B. Y., Chandu, K., Dziri, N., Kumar, S., Zick, T., Choi, Y., et al. (2025) · Findings of the Association for Computational Linguistics: NAACL 2025

Natural Language Processing TechniquesTopic Modeling

Cited by 13

s1: Simple test-time scaling

Muennighoff, N., Yang, Z., Shi, W., Li, X. L., Fei-Fei, L., Hajishirzi, H., Zettlemoyer, L., Liang, P., Candès, E., & Hashimoto, T. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 12

TÜLU 3: Pushing Frontiers in Open Language Model Post-Training

Lambert, N., Morrison, J., Pyatkin, V., Huang, S., Ivison, H., Brahman, F., Miranda, L. J. V., Liu, A., Dziri, N., Lyu, S., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 12