Hierarchical Virtual Token Prompts for Accurate LLM Customization
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Solution Overview
Problem
Existing language models struggle with accuracy in natural language processing tasks due to the inefficiencies of fine-tuning methods, which can lead to biased outputs and loss of diversity, and few-shot learning requires significant trial and error to find effective prompts.
Innovation Solution
A hierarchical virtual token generator (VTG) architecture is employed to customize language models by training multiple levels of VTGs using user inputs and datasets, maintaining the model's full memory capacity while improving accuracy through user corrections and adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fine-tuning methods are used to customize language models, then accuracy for particular tasks is improved, but computational resources and time required for training increase significantly
Solution Approach 1:
The patent creates a virtual token generator that generates customized prompts by transforming input data through a neural network, effectively copying the language model's capabilities without requiring actual fine-tuning of the model weights. This allows task-specific customization using minimal computational resources while maintaining the original model's accuracy.
Solution Approach 2:
The virtual token generator acts as an intermediary between the input data and the language model. It transforms raw inputs into customized prompts that the language model can process, enabling task-specific adaptation without directly modifying the model itself, thus reducing computational requirements.
2Measurement precision
If fine-tuning methods are used to customize language models, then accuracy for particular tasks is improved, but the model's diversity and original capabilities are lost
Solution Approach 1:
The virtual token generator creates customized prompts that preserve the language model's original capabilities while adapting to specific tasks. The generated prompts maintain the model's diverse response patterns and original training data characteristics, preventing the loss of diversity that occurs with traditional fine-tuning.
Solution Approach 2:
The system separates the customization function from the language model itself by using a virtual token generator that operates independently. This segmentation allows the language model to maintain its original capabilities while the virtual generator handles task-specific customization, preserving both accuracy and diversity.
3Measurement precision
If few-shot learning with trial and error is used to find effective prompts, then task-specific performance is improved, but time required for optimization increases significantly
Solution Approach 1:
The virtual token generator performs preliminary transformation of input data into optimized prompts before they are processed by the language model. By pre-computing the customized prompts through the neural network, the system eliminates the need for time-consuming trial and error during task execution, achieving fast task-specific performance.
Solution Approach 2:
The virtual token generator automatically optimizes prompts for specific tasks without requiring manual intervention or iterative trial and error. The neural network self-adjusts to generate effective prompts based on the input data, saving significant time that would otherwise be spent on manual optimization.
Data Source
AI summary
Disclosed are systems and techniques that may generate prompts for language models. The techniques include obtaining a first dataset and a second dataset and training a hierarchical virtual token generator (VTG) model to generate a large language model (LLM) input prompt. Training the hierarchical VTG includes training, based on the first dataset, a first VTG to output a first virtual token and training, based on the second dataset, a second VTG to output a second virtual token embedding. The generated LLM input prompt includes the first virtual token embedding and the second virtual token embedding.