Parameter-Efficient Transfer Learning for NLP
- Thesis: To support continual/online learning w/o incurring too much cost for fine-tuning task-specific model, the proposed solution keep the pre-trained weights frozen and adds two adapter layers for each transformer layer. This reduces the number of trainable parameters to 3-8% of the original model. With this setup, each downstream task would have its own weights in the adapter layers but all of the tasks share the pre-trained weights. The performance is almost similar to the full fine-tuning.
- Method(s):
- Each transformer layer has two adapter layers:
- One after projection layer of multi-head attention but before skip connection
- One after feedforward layer but before skip connection
- Each adapter layer has:
- Feedforward layer to down-project input -> creates bottleneck. The smaller the bottleneck -> less parameters at cost of less performance
- Followed by non-linearity
- Feedforward layer to up-project input to original size
- Skip connection of input and output of Feedforward up-project
- Training:
- Weights in adapter layers
- Layernorms in each transformer layer
- Each transformer layer has two adapter layers:
#nlp #llm #fine-tuning
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