Fast Vocabulary Transfer for Language Model Compression
This addresses the need for efficient language models in business settings, but it is incremental as it builds on existing compression techniques.
The paper tackled the problem of balancing language model performance and size for business applications by proposing vocabulary transfer for model compression, achieving significant reductions in model size and inference time with only marginal performance loss.
Real-world business applications require a trade-off between language model performance and size. We propose a new method for model compression that relies on vocabulary transfer. We evaluate the method on various vertical domains and downstream tasks. Our results indicate that vocabulary transfer can be effectively used in combination with other compression techniques, yielding a significant reduction in model size and inference time while marginally compromising on performance.