Probing the Pitfalls: Understanding SVD’s Shortcomings in Language Model Compression - Сергей Александрович Плетенев (Journal of Language and Education)
Background: Modern computational linguistics heavily relies on large language models that demonstrate strong performance in various Natural Language Inference (NLI) tasks. These models, however, require substantial computational resources for both training and deployment. To address this challenge, a range of compression and acceleration techniques has been developed, including quantization, pruning, and factorization. Each of these approaches operates differently, can be applied at various levels of the model architecture, and is suited to different deployment scenarios.