Reinforcement Learning for Neural Network Domain Adaptation
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Solution Overview
Problem
General purpose pre-trained models face performance limitations when applied to service domains requiring specific knowledge, such as medical care or shopping, due to cost constraints and the need for large-scale training data.
Innovation Solution
An electronic device capable of performing reinforcement learning on an artificial neural network model based on a large pre-trained model, which generates response candidates for user utterances and selects the best response based on performance indicators, allowing for optimization with a small amount of training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general purpose pre-trained models are used, then they can provide stable performance for various domains, but they face performance limitations when applied to service domains requiring specific knowledge
Solution Approach 1:
The patent segments the training data into general domain data and specific domain data, and segments the model training into pre-training phase and fine-tuning phase. The artificial neural network model is first pre-trained on general domain data to learn universal patterns, then fine-tuned on specific domain data to adapt to domain-specific requirements, thereby resolving the contradiction between versatility and domain-specific performance
Solution Approach 2:
The patent applies preliminary action by performing pre-training on large-scale general domain data before fine-tuning on specific domain data. This preliminary pre-training equips the model with general knowledge and capabilities, which then serves as a foundation for subsequent domain-specific adaptation, enabling the model to achieve both versatility and domain-specific reliability
2Reliability
If large-scale training data is used for pre-trained models, then stable performance is achieved, but cost limitations prevent implementation in small businesses
Solution Approach 1:
The patent leverages pre-trained models that have already undergone preliminary training on large-scale data by major technology companies. Small businesses can directly utilize these pre-trained models without incurring the high costs of collecting and processing large-scale training data, thereby achieving stable performance at lower implementation costs
Solution Approach 2:
The patent enables small businesses to copy and utilize the knowledge embedded in large pre-trained models through fine-tuning on smaller domain-specific datasets. Instead of creating models from scratch requiring massive data, businesses can replicate the successful patterns learned by large models and adapt them to their specific domains, significantly reducing implementation costs while maintaining performance stability
3Quantity of substance
If manual labeling tasks are performed to build training data, then large-scale training data is prepared, but it increases time and resource consumption
Solution Approach 1:
The patent copies training data from public domains and existing sources rather than creating all training data through manual labeling. By reusing and adapting existing datasets, the system achieves large-scale training data preparation with significantly reduced time and resource investment compared to creating data from scratch
Solution Approach 2:
The patent creates training data that serves multiple functions: general domain data for pre-training provides foundational knowledge, while domain-specific data for fine-tuning adapts the model to specific applications. This multi-functional data strategy maximizes the utility of training data without requiring separate extensive labeling efforts for each domain
Data Source
AI summary
An electronic device according to an embodiment in this document includes a memory configured to store an artificial neural network model and a processor functionally connected to the memory, wherein the processor obtains a user's current utterance, generates a plurality of response candidates according to the current utterance using the artificial neural network model, and performs reinforcement learning on the artificial neural network model by selecting a response according to the current utterance that best matches a specified criterion including a performance indicator from among the plurality of response candidates, using a large pre-trained model.