Prefix-Tuning: Optimizing Continuous Prompts for Generation
- Thesis: Train continuous task-specific context vectors that will steer the LM based on the task. This is very efficient because only the continuous vectors for the prefix that will be trained and all other pre-trained weights are kept frozen. This approach is driven from the success of prompting and in-context learning that steered the LM to generate tokens based on the examples provided in the prompt. The drawback of in-context learning is that LLMs have limited context window so including examples and other instructions would reduce the number of tokens for the task we want the LLM to perform.
- Method(s):
- Train continuous task-specific vectors on upstream tasks that will steer the LM for different tasks
- Such prefix is kind of considered as “virtual tokens” and subsequent (actual) tokens would attend to them
- Training: Each transformer block will have its own continuous task-specific tensor which is obtained by two linear layers with nonlinearity in-between. Then the output will be concatenated with the input embedding. For inference: we have to supply prefix prompt for the task we’re performing
#nlp #llm #fine-tuning
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