Library news

• Small Language Models: “The Big Shrink in LLMs”

  02-03-2025 19:00 via infodocket.com
  From Communications of the ACM:
  …smaller models and datasets have emerged as a solution to some of their larger cousins’ drawbacks. Techniques such as knowledge distillation, transferring knowledge from large to smaller models, and pruning, removing model parameters (such as weight or temperature) without degrading accuracy, are also supporting the shrink. This and developments in edge computing enabled by smaller models that can run ‘on device’ raise questions about the
  
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