Aghajanyan, Armen

Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Aghajanyan, A., Zettlemoyer, L., & Gupta, S. (2020) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 374

InCoder: A Generative Model for Code Infilling and Synthesis

Fried, D., Aghajanyan, A., Lin, J., Wang, S., Wallace, E., Shi, F., Zhong, R., Yih, W.-T., Zettlemoyer, L., & Lewis, M. (2022) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 187

Muppet: Massive Multi-task Representations with Pre-Finetuning

Aghajanyan, A., Gupta, A., Shrivastava, A., Chen, X., Zettlemoyer, L., & Gupta, S. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 183

Improving Passage Retrieval with Zero-Shot Question Generation

Sachan, D., Lewis, M., Joshi, M., Aghajanyan, A., Yih, W.-T., Pineau, J., & Zettlemoyer, L. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Information Retrieval and Search BehaviorTopic Modeling

Cited by 90

Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Tirumala, K., Markosyan, A., Zettlemoyer, L., & Aghajanyan, A. (2022) · Advances in Neural Information Processing Systems 35

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 67

CM3: A Causal Masked Multimodal Model of the Internet

Aghajanyan, A., Huang, B., Ross, C., Karpukhin, V., Xu, H., Goyal, N., Okhonko, D., Joshi, M., Ghosh, G., Lewis, M., et al. (2022) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 42

HTLM: Hyper-Text Pre-Training and Prompting of Language Models

Aghajanyan, A., Okhonko, D., Lewis, M., Joshi, M., Xu, H., Ghosh, G., & Zettlemoyer, L. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 35

D4: Improving LLM Pretraining via Document De-Duplication and Diversification

Tirumala, K., Simig, D., Aghajanyan, A., & Morcos, A. (2023) · Advances in Neural Information Processing Systems 36

Topic ModelingNatural Language Processing Techniques

Cited by 28

Scaling Laws for Generative Mixed-Modal Language Models

Aghajanyan, A., Yu, L., Conneau, A., Hsu, W.-N., Hambardzumyan, K., Zhang, S., Roller, S., Goyal, N., Levy, O., & Zettlemoyer, L. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 10