The dynamic-width speculative beam decoding (DSBD) approach, while primarily designed for transformer-based large language models (LLMs), can be adapted for other architectures such as recurrent neural networks (RNNs) or convolutional neural networks (CNNs) used in natural language processing tasks. To extend DSBD to these models, several modifications would be necessary:
Model Compatibility: The first step would involve ensuring that the auxiliary model used for speculative decoding is compatible with the architecture of the target LLM. For instance, if using an RNN, the draft generation mechanism would need to account for the sequential nature of RNNs, potentially requiring modifications to how draft tokens are generated and verified.
Draft and Verification Scheme: The draft and verification scheme in DSBD could be adapted to work with the output distributions of non-transformer models. This would involve redefining how the draft beams are sampled and how the verification process is conducted, ensuring that the probabilistic distributions align with the model's architecture.
Dynamic Beam Width Adjustment: The adaptive mechanism for adjusting the beam width based on context could be implemented in other architectures by analyzing the output distributions of the auxiliary model and the main model. This would require a thorough understanding of how different architectures handle token generation and the associated probabilities.
Parallel Verification: The forest-based parallel verification mechanism could be adapted to utilize the specific characteristics of the model architecture. For instance, in RNNs, the sequential dependencies would need to be respected, potentially leading to a different approach to managing key-value caches and attention mechanisms.
Performance Evaluation: Finally, extensive empirical testing would be necessary to evaluate the performance of the adapted DSBD approach on various tasks and datasets, ensuring that the efficiency and effectiveness gains observed in transformer models are replicated or improved upon in other architectures.