Researchers at Stanford Propose a Family of Representation Finetuning (ReFT) Methods that Operates on a Frozen Base Model and Learn Task-Specific Interventions on Hidden Representations

Pretrained language models (LMs) are commonly finetuned to adapt them to new domains or tasks, a process known as finetuning. While finetuning allows for adaptation to various functions with small amounts of in-domain data, it can be prohibitively expensive for large LMs.  Parameter-efficient finetuning (PEFT) methods offer a solution by updating only a fraction of […]

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