ReFT: Representation Finetuning for Language Models | AI Paper Explained
Can LoReFT be a rival for LoRA? According to ReFT paper, it has the potential to replace LoRA in various cases.
In this video we dive into the research paper that presents ReFT and LoReFT. We'll explain what is representation fine-tuning (ReFT), and how it is different than previous parameter-efficient fine-tuning (PEFT) methods, such as LoRA.
ReFT is a family of methods that can be used to adapt pre-trained transformer models to specific tasks, and we'll understand the concept of how such methods work. Specifically, the paper presents a concrete ReFT method which is called LoReFT, which stands for Low-rank Linear Subspace ReFT. We'll explain how it works, and see results from the paper that show the great potential of this method comparing to previous PEFT methods.
Paper page - https://arxiv.org/abs/2404.03592
GitHub repo -https://github.com/stanfordnlp/pyreft
Code for LoReFT - https://github.com/stanfordnlp/pyreft...
Blog post - https://aipapersacademy.com/reft/
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Chapters:
0:00 Introduction & Motivation
1:49 What is ReFT?
3:47 ReFT & LoReFT Details
6:07 LoReFT Results