Prefix-Tuning is a lightweight alternative to fine-tuning for natural language generation tasks, optimizing continuous task-specific vectors.
Prefix-Tuning is a method proposed as a lightweight alternative to traditional fine-tuning for large pretrained language models, specifically for natural language generation tasks. Unlike fine-tuning, which modifies all language model parameters and requires storing a full copy for each task, prefix-tuning keeps the language model parameters frozen. Instead of modifying the entire model, prefix-tuning optimizes a sequence of continuous task-specific vectors, referred to as the "prefix." This approach is inspired by prompting for language models, allowing subsequent tokens to attend to this prefix as if they were "virtual tokens." The method has been applied to models like GPT-2 for table-to-text generation and BART for summarization. Research shows that prefix-tuning can achieve comparable performance to fine-tuning in full data settings while learning only 0.1% of the parameters. It also outperforms fine-tuning in low-data scenarios and demonstrates better extrapolation to examples with topics not seen during training.
Platforms: Web
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