Autoregressive Language Model Switching for Lower Token Cost
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Solution Overview
Problem
State-of-the-art language models generate long sentences computationally expensively due to token-by-token generation, making them inefficient for tasks requiring less computational resources.
Innovation Solution
Implement reinforcement learning (RL) to dynamically select between different language models based on previously generated tokens, optimizing computational efficiency and accuracy by switching between large and small models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If state-of-the-art language models generate long sentences using token-by-token generation, then output accuracy and detail are improved, but computational cost becomes extremely expensive
Solution Approach 1:
The patent applies dynamics by making the language model selection adaptive and time-varying. The RL agent dynamically switches between different language models based on the generation state, using larger models for critical tokens and smaller models for less critical tokens, thereby optimizing the balance between accuracy and computational cost throughout the generation process
Solution Approach 2:
The patent changes the parameter of model size dynamically during generation. By having an RL agent select from multiple language models with different sizes and complexities, the system adjusts computational resources allocated to each token generation step, reducing overall computational cost while maintaining output quality
2Measurement precision
If a single large language model is used for all tokens, then output accuracy is maintained, but computational resources are wasted on easy tokens
Solution Approach 1:
The patent applies local quality by assigning different model resources to different parts of the generation process. The RL agent identifies which tokens are difficult to generate and require larger models, while using smaller models for easier tokens, thereby optimizing computational resources at the local token level rather than uniformly across the entire generation
Solution Approach 2:
The patent uses partial action by applying large language models only partially to the generation task - specifically to tokens where they are most needed - rather than excessively applying them to all tokens. This selective application reduces computational waste while maintaining accuracy where it matters most
3Productivity
If multiple language models are used dynamically, then computational efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces an RL agent as an intermediary component that manages the complexity of switching between multiple language models. This mediator automatically handles model selection based on generation state, abstracting the complexity from the user and enabling efficient dynamic model usage without requiring manual intervention or complex user-side management
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for machine learning. In an example method, a first output generated by a first language model, of a plurality of language models, based on an input prompt is accessed. A second language model is selected, from the plurality of language models, to generate a second output for the input prompt based on processing the first output using a reinforcement learning (RL) agent. Generation of a response to the input prompt is facilitated based on the first output and the second output, comprising causing the first output to be provided as input to the second language model.