What Exactly Happens When We Fine-Tune Large Language Models?
Fine-tuning has long been the standard approach for adapting LLMs to downstream tasks, but only recent research is starting to reveal what's happening under the hood
Google’s BERT (Devlin et al 2019) was a paradigm shift in natural language modeling, in particular because of its introduction of the pre-training / fine-tuning paradigm: after unsupervised pre-training on a massive amount of text data, the model can be rapidly fine-tuned on a specific downstream task with relatively few labels because generic linguist…
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