Holtzman, Ari

QLoRA: Efficient Finetuning of Quantized LLMs

Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 3,832

The Curious Case of Neural Text Degeneration

Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,304

CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Hessel, J., Holtzman, A., Forbes, M., Le Bras, R., & Choi, Y. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Multimodal Machine Learning ApplicationsImage Retrieval and Classification Techniques

Cited by 1,232

HellaSwag: Can a Machine Really Finish Your Sentence?

Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 830

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

Defending Against Neural Fake News

Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., & Choi, Y. (2019) · arXiv

Misinformation and Its ImpactsAdversarial Robustness in Machine Learning

Cited by 569

Learning to Write with Cooperative Discriminators

Holtzman, A., Buys, J., Forbes, M., Bosselut, A., Golub, D., & Choi, Y. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 192

Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right

Holtzman, A., West, P., Shwartz, V., Choi, Y., & Zettlemoyer, L. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 127

CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving

Liu, Y., Li, H., Cheng, Y., Ray, S., Huang, Y., Zhang, Q., Du, K., Yao, J., Lu, S., Ananthanarayanan, G., et al. (2024) · Proceedings of the ACM SIGCOMM 2024 Conference

Topic ModelingNatural Language Processing Techniques

Cited by 118

Do Neural Language Representations Learn Physical Commonsense?

Forbes, M., Holtzman, A., & Choi, Y. (2019) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 60

How FaR Are Large Language Models From Agents with Theory-of-Mind?

Zhou, P., Madaan, A., Potharaju, S. P., Gupta, A., McKee, K. R., Holtzman, A., Pujara, J., Ren, X., Mishra, S., Nematzadeh, A., et al. (2023) · arXiv

Topic ModelingChild and Animal Learning Development

Cited by 6

CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving

Liu, Y., Li, H., Cheng, Y., Ray, S., Huang, Y., Zhang, Q., Du, K., Yao, J., Lu, S., Ananthanarayanan, G., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0

Predicting vs. Acting: A Trade-off Between World Modeling & Agent Modeling

Li, M., Shi, W., Pagnoni, A., West, P., & Holtzman, A. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0