PrefixTuning

Prefixtuning, commonly called prefix-tuning, is a parameter-efficient fine-tuning method for large pretrained language models. It trains a small set of task-specific, continuous prefix embeddings while keeping the base model largely frozen. The learned prefixes act as soft prompts that condition the model’s behavior without updating its full weights.

In practice, the method inserts trainable prefix vectors into the model’s attention mechanisms across layers. For each layer, a sequence of key and value vectors (the prefix) is prepended to the layer’s existing keys and values, effectively guiding how the model attends to inputs. Only the prefix embeddings are updated during training, typically resulting in a modest number of trainable parameters relative to full fine-tuning. This makes prefix-tuning suitable for adapting very large models to new tasks or domains with limited labeled data and compute.

Advantages of prefix-tuning include parameter efficiency, fast adaptation, and the ability to reuse a single large model for multiple tasks by storing a separate prefix per task. It often delivers competitive performance to full fine-tuning on a range of natural language processing tasks, especially in settings with constrained data or resources.

Limitations include potential reductions in performance on tasks requiring substantial model reorganization, sensitivity to the choice of prefix length and layer placement, and architectural constraints that may limit applicability across all model families. Prefix-tuning is one of several parameter-efficient fine-tuning strategies, alongside soft prompting and other adapter-based approaches, and is frequently discussed in the context of prompt-based learning for large language models.