Srinath Srinivasan

The Cost of Compression: Investigating the Impact of Compression on Parametric Knowledge in Language Models

Compressing large language models (LLMs), often consisting of billions of parameters, provides faster inference, smaller memory footprints, and enables local deployment. The standard compression techniques are pruning and quantization, with the former eliminating redundant connections in model laye…