ReFT: Representation Finetuning for Language Models

**Paper**: [https://arxiv.org/abs/2404.03592](https://arxiv.org/abs/2404.03592)

**Code**: [https://github.com/stanfordnlp/pyreft](https://github.com/stanfordnlp/pyreft)

**Abstract**:

>Parameter-efficient fine-tuning (PEFT) methods seek to adapt large models via updates to a small number of weights. However, much prior interpretability work has shown that representations encode rich semantic information, suggesting that editing representations might be a more powerful alternative. Here, we pursue this hypothesis by developing a family of **Representation Finetuning** (**ReFT**) methods. ReFT methods operate on a frozen base model and learn task-specific interventions on hidden representations. We define a strong instance of …

abstract languagetechnology