Parameter Efficient Fine-Tuning (PEFT) has gained significant attention for its ability to achieve competitive results while updating only a small subset of trainable parameters. Despite the promising performance of current PEFT methods, they present challenges in hyperparameter selection, such as determining the rank of LoRA or Adapter, or specifying the length of soft prompts. In addressing these challenges, we propose a novel approach to fine-tuning neural models, termed Representation EDiting (RED), which scales and biases the representation produced at each layer. RED substantially reduces the number of trainable parameters by a factor of $25,700$ compared to full parameter fine-tuning, and by a factor of $32$ compared to LoRA. Remarkably, RED achieves comparable or superior results to full parameter fine-tuning and other PEFT methods. Extensive experiments were conducted across models of varying architectures and scales, including RoBERTa, GPT-2, T5, and Llama-2, and the results demonstrate the efficiency and efficacy of RED, positioning it as a promising PEFT approach for large neural models.

翻译：参数高效微调（PEFT）因其仅更新少量可训练参数即可取得竞争性结果而受到广泛关注。尽管当前PEFT方法展现出令人期待的性能，但它们在超参数选择方面仍面临挑战，例如确定LoRA或Adapter的秩，或指定软提示的长度。针对这些挑战，我们提出了一种新颖的神经模型微调方法，称为表示编辑（RED），该方法对每一层生成的表示进行缩放和偏置调整。与全参数微调相比，RED将可训练参数数量大幅减少约25,700倍，与LoRA相比减少约32倍。值得注意的是，RED取得了与全参数微调及其他PEFT方法相当或更优的结果。我们在不同架构和规模的模型（包括RoBERTa、GPT-2、T5和Llama-2）上进行了广泛实验，结果证明了RED的效率和有效性，使其成为大型神经模型中一种有前景的PEFT方法。