Neural-Network Persona Customization Without Full Retraining
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Solution Overview
Problem
Pretrained neural networks, such as large language models (LLMs), are inflexible and difficult to retrain due to the high cost and time required, limiting their adaptability to individual user needs.
Innovation Solution
A computer system that customizes the output of a pretrained neural network by accessing stored information associated with a persona, modifying prompts based on this information, and generating outputs tailored to individual users, allowing for flexible adaptation without extensive retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained on massive datasets to provide accurate outputs, then the accuracy and capability of the neural network is improved, but the cost and time required for retraining increases significantly
Solution Approach 1:
The patent segments the training process into two distinct phases: (1) initial training on massive datasets to establish base capabilities, and (2) subsequent customization on small datasets to adapt to specific users. This segmentation allows the system to maintain high accuracy from the initial training while enabling rapid, low-cost customization without requiring full retraining.
Solution Approach 2:
The patent performs preliminary training on massive datasets before deployment, establishing the neural network's base capabilities in advance. This preliminary action ensures that when customization is needed, the system already possesses the foundational knowledge and skills, requiring only minor adjustments rather than complete retraining.
2Measurement precision
If neural networks are trained on massive datasets to provide accurate outputs, then the capability of the neural network is improved, but the flexibility and adaptability to individual user needs deteriorates
Solution Approach 1:
The patent divides the neural network system into a base model component (trained on massive datasets for general accuracy) and a customization layer (adapted to individual users). This segmentation enables the system to maintain high baseline accuracy while providing flexible adaptation to different user needs through separate, targeted training processes.
Solution Approach 2:
The patent implements a dynamic system where the neural network can be easily reconfigured for different users through lightweight customization training. The system transitions from a static, fixed model to a dynamic, adaptable model that can be quickly adjusted to individual user preferences and requirements without compromising base capabilities.
3Adaptability or versatility
If neural networks are retrained frequently to adapt to user needs, then the adaptability is improved, but the cost and computational resources required increases
Solution Approach 1:
The patent separates the training workload into an initial heavy training phase on massive datasets and subsequent lightweight customization phases on small datasets. This segmentation dramatically reduces the computational cost of frequent retraining, as the customization phase requires only processing of small, user-specific datasets rather than full-scale training.
Solution Approach 2:
The patent changes the training parameters for the customization phase, using smaller dataset sizes and potentially adjusted training intensity compared to the initial training phase. This parameter change enables frequent customization with minimal computational overhead, as the system only needs to learn user-specific patterns rather than general knowledge.
Data Source
AI summary
A computer system that customizes an output provided by a pretrained neural network is described. During operation, the computer system may receive a prompt (or input) associated with a first individual. Then, the computer system may access stored information (in memory) associated with a persona associated with a second individual. For example, the second individual may be different from the first individual. However, in some embodiments, the second individual may be the same as the first individual. Note that the stored information may include memories associated with one or more different sessions of interacting with the pretrained neural network than a current session. Moreover, the computer system may modify the prompt based at least in part on the persona associated with the second individual. Next, the computer system may generate, using the pretrained neural network, the output based at least in part on the modified prompt.