Edge 331: Universal Language Model Finetuning

One off the earliest fine-tuning techniques that still works today.

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In this Issue:

1. ULMFiT one of the first fine-tuning methods ever created.

2. Google’s Symbol Tuning research papers.

3. Scale’s LLM engine.

💡 ML Concept of the Day: Universal Language Model Finetuning

To understand fine-tuning in foundation models, its sometimes useful to go back to some of the early methods. One of the very early successful attempts to introduce fine-tuning in language models was the Universal Language Model FIne-Tuning(ULMFiT). Conceptually, ULMFiT is a transfer learning method that adapts a language model to specific tasks.

ULMFiT adopts a robust approach to language modeling by leveraging a large corpus in a general domain for pretraining, followed by innovative fine-tuning techniques on the target task. The brilliance of this method lies in its universality, as it satisfies several crucial practical criteria: