| Scaling down to scale up: A guide to parameter-efficient fine-tuning V Lialin, V Deshpande, A Rumshisky arXiv preprint arXiv:2303.15647, 2023 | 196 | 2023 |
| Relora: High-rank training through low-rank updates V Lialin, N Shivagunde, S Muckatira, A Rumshisky arXiv preprint arXiv:2307.05695, 2023 | 105* | 2023 |
| Learning to ask like a physician E Lehman, V Lialin, KY Legaspi, AJR Sy, PTS Pile, NRI Alberto, ... arXiv preprint arXiv:2206.02696, 2022 | 24 | 2022 |
| Named entity recognition in noisy domains V Malykh, V Lyalin 2018 international conference on artificial intelligence applications and …, 2018 | 12 | 2018 |
| Honey, I shrunk the language: Language model behavior at reduced scale V Deshpande, D Pechi, S Thatte, V Lialin, A Rumshisky arXiv preprint arXiv:2305.17266, 2023 | 10 | 2023 |
| Update frequently, update fast: Retraining semantic parsing systems in a fraction of time V Lialin, R Goel, A Simanovsky, A Rumshisky, R Shah arXiv preprint arXiv:2010.07865, 2020 | 10* | 2020 |
| Scalable and accurate self-supervised multimodal representation learning without aligned video and text data V Lialin, S Rawls, D Chan, S Ghosh, A Rumshisky, W Hamza Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 8 | 2023 |
| Life after BERT: What do Other Muppets Understand about Language? V Lialin, K Zhao, N Shivagunde, A Rumshisky arXiv preprint arXiv:2205.10696, 2022 | 8 | 2022 |
| Relora: High-rank training through low-rank updates, 2023 V Lialin, N Shivagunde, S Muckatira, A Rumshisky URL https://arxiv. org/abs/2307.05695, 0 | 8 | |
| Let's reinforce step by step S Pan, V Lialin, S Muckatira, A Rumshisky arXiv preprint arXiv:2311.05821, 2023 | 6 | 2023 |
| Scaling down to scale up: A guide to parameter-efficient fine-tuning (2023) V Lialin, V Deshpande, A Rumshisky arXiv preprint arXiv:2303.15647, 2023 | 6 | 2023 |
| Narrativetime: Dense temporal annotation on a timeline A Rogers, M Karpinska, A Gupta, V Lialin, G Smelkov, A Rumshisky arXiv preprint arXiv:1908.11443, 2019 | 5 | 2019 |
| Deconstructing in-context learning: Understanding prompts via corruption N Shivagunde, V Lialin, S Muckatira, A Rumshisky arXiv preprint arXiv:2404.02054, 2024 | 4 | 2024 |
| Recent advances, applications, and open challenges in machine learning for health: Reflections from research roundtables at ml4h 2023 symposium H Jeong, S Jabbour, Y Yang, R Thapta, H Mozannar, WJ Han, ... arXiv preprint arXiv:2403.01628, 2024 | 4 | 2024 |
| Scaling down to scale up: A guide to parameter-efficient fine-tuning. arXiv 2023 V Lialin, V Deshpande, A Rumshisky arXiv preprint arXiv:2303.15647, 0 | 4 | |
| Emergent abilities in reduced-scale generative language models S Muckatira, V Deshpande, V Lialin, A Rumshisky arXiv preprint arXiv:2404.02204, 2024 | 1 | 2024 |
| Improving Classification Robustness for Noisy Texts with Robust Word Vectors V Malykh, V Lyalin Journal of Mathematical Sciences 273 (4), 605-613, 2023 | 1 | 2023 |
| Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning N Shivagunde, V Lialin, A Rumshisky arXiv preprint arXiv:2303.16445, 2023 | | 2023 |
| Injecting Hierarchy with U-Net Transformers D Donahue, V Lialin, A Rumshisky arXiv preprint arXiv:1910.10488, 2019 | | 2019 |
| К вопросу о классификации зашумленных текстов ВА Малых, ВА Лялин Труды Института системного анализа Российской академии наук 68 (S1), 174-182, 2018 | | 2018 |