Language Model Compression

Language model compression aims to reduce the substantial size and computational demands of large language models (LLMs) without significantly sacrificing performance. Current research focuses on techniques like structured pruning (e.g., K-prune), matrix factorization (enhanced by methods such as Fisher-weighted SVD), and knowledge distillation (including task-aware and attribution-driven approaches), often applied to transformer-based architectures. These methods address challenges such as maintaining accuracy across multiple languages (especially low-resource ones) and efficiently handling the complex interactions within LLMs. Successful compression techniques are crucial for deploying LLMs on resource-constrained devices and improving the accessibility and scalability of natural language processing applications.