Large language models (LLMs) face computational cost barriers hindering broad deployment, especially in autoregressive generation. A study by Google Research and DeepMind introduces Tandem Transformers, prioritizing natural language understanding (NLU) over generation (NLG). Tandem’s efficiency and accuracy in applications make it a promising advancement for LLMs. For more information, refer to the Paper.
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Introducing Tandem Transformers for Inference Efficient Large Language Models (LLMs)
Very large language models (LLMs) are facing computational cost barriers, which limits their broad deployment. Modern LLM designs bind natural language understanding (NLU) and natural language generation (NLG) together, making autoregressive answer creation less efficient.
Tandem Transformers for Improved Efficiency
A new study by Google Research and DeepMind presents Tandem Transformers, a new design that gives NLU a larger share of the model’s resources than NLG. This design separates the capacity required for NLU and NLG, resulting in a more efficient model without sacrificing accuracy.
Tandem Transformers Recommendations
The researchers recommend using Tandem + SPEED framework for applications that require high output quality indistinguishable from the main model. This framework uses Tandem small model to create draft tokens and the large model to verify them, resulting in improved draft quality while reducing verification overhead.
Evaluation and Empirical Results
The researchers find that Tandem + SPEED with distillation can outperform the baseline model by a factor of at least 2.19 on various datasets while maintaining the same output quality. Additionally, Tandem’s latency can be further reduced on various datasets. To learn more, check out the paper.
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