Phased Observational Learning for Language Model Training
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Solution Overview
Problem
Traditional methods for training and updating language models for intelligent virtual assistants require significant human involvement, leading to resource-intensive and time-consuming processes, with performance decay over time due to infrequent updates and the use of low-quality or unreliable data from crowdsourcing.
Innovation Solution
Implementing a phased observational learning approach that combines self-supervised and reinforcement learning, where a pre-trained language model is updated offline using historical transcripts and then fine-tuned online based on customer interactions, allowing for continuous training and performance evaluation without direct human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional human-involved methods are used to train and update language models, then model performance can be improved through quality data, but the process becomes resource-intensive and time-consuming
Solution Approach 1:
The system enables self-service training by having the language model learn from historical transcripts and customer interactions automatically. The model updates itself through self-supervised learning on transcripts and reinforcement learning from interaction feedback, eliminating the need for manual human involvement in data collection and model training while maintaining high performance through continuous autonomous learning.
Solution Approach 2:
The patent implements continuous training through two phases: offline self-supervised learning that continuously updates the model on historical transcripts, and online reinforcement learning that continuously adapts the model based on customer interactions. This continuous learning process prevents performance decay and maintains high model effectiveness without requiring periodic resource-intensive retraining campaigns.
2Use of energy by moving object
If language models are updated infrequently, then resource consumption is reduced, but performance decay occurs over time
Solution Approach 1:
The system implements periodic action through structured training phases: offline training on historical transcripts followed by online training on customer interactions. This phased approach allows the model to learn systematically from different data sources in sequence, maintaining performance through regular updates while managing computational resources efficiently by alternating between training and deployment phases.
Solution Approach 2:
The patent applies preliminary action by performing offline self-supervised learning on historical transcripts before deploying the model for customer interactions. This preliminary training prepares the model with foundational knowledge from high-quality historical data, enabling it to perform better during online reinforcement learning phases and reducing the overall training time and resource requirements.
3Quantity of substance
If crowdsourcing is used to collect training data, then data quantity increases, but data quality becomes unreliable
Solution Approach 1:
The system extracts high-quality training data from historical customer service transcripts and customer interaction logs, removing the need for crowdsourcing. By taking out and utilizing existing structured data from real customer service scenarios, the model receives high-quality training data that accurately reflects actual usage patterns while maintaining data quantity through comprehensive historical datasets.
4Measurement precision
If direct human intervention is used in model training, then training accuracy can be monitored, but training time and complexity increase
Solution Approach 1:
The patent implements feedback mechanisms through reinforcement learning where the model receives feedback from customer interactions and performance metrics. The system automatically monitors training accuracy by evaluating model performance on customer queries and uses this feedback to continuously improve through reinforcement learning, eliminating the need for direct human monitoring while maintaining precise measurement of training effectiveness through automated evaluation metrics.
Data Source
AI summary
Disclosed embodiments pertain to training an intelligent virtual assistant through phased observational learning tasks. A pre-trained language model can be updated offline to produce a second language model with self-supervised learning based on transcripts of historical interactions between one or more customers, one or more customer service agents, and one or more data stores. The second language model can be evaluated and determined to satisfy a predetermined performance threshold. Subsequently, the second language model can be updated online to produce a third language model with reinforcement learning based on received customer input and similarity between a response provided by a customer service agent and a predicted response generated by the second language model. The third language model can then be deployed with an intelligent virtual assistant to respond to received user input.