Synthetic Training Data Generation for Data-Scarce Model Fine-Tuning
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Solution Overview
Problem
Current methods for regulating AI bots require large amounts of data and human feedback to ensure appropriate responses, which is not feasible in data scarce settings.
Innovation Solution
A system and method for generating training data using synthetic queries and responses based on rules, aggregating these responses to train a preference model, and fine-tuning an SFT model to provide real-time, rule-compliant responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large amounts of data and human feedback are used to train AI bots, then the AI bot can generate correct and compliant responses, but the system becomes infeasible in data scarce settings
Solution Approach 1:
The patent uses synthetic data generation to create copies of training data that mimic real customer interactions. The system generates synthetic queries and responses that replicate the characteristics of real data, allowing the model to be trained without requiring actual customer interaction data. This copying approach enables reliable training in data scarce environments.
Solution Approach 2:
The patent introduces an intermediary data generation system that bridges the gap between real data scarcity and training requirements. The system uses a data generation model that creates synthetic training data based on templates and patterns, serving as a mediator that provides sufficient training material without direct access to real customer data.
2Reliability
If human feedback is required to regulate AI bots, then the AI bot can be ensured to follow codes of conduct, but the process becomes complex and resource-intensive
Solution Approach 1:
The patent implements self-service regulation where the AI bot trains itself on synthetic data that encodes codes of conduct. Instead of requiring continuous human feedback and intervention, the system generates compliant responses autonomously by learning from pre-generated synthetic training data that incorporates regulatory guidelines.
Solution Approach 2:
The patent applies preliminary action by pre-generating synthetic training data that incorporates codes of conduct before the AI bot needs to operate. The regulatory guidelines are embedded in the synthetic data during the training phase, allowing the model to learn compliance rules in advance without requiring real-time human supervision or feedback during operation.
3Quantity of substance
If synthetic data is generated using multiple models, then training data can be created in data scarce settings, but the process requires aggregation of multiple models
Solution Approach 1:
The patent merges multiple models into a unified data generation system. The data generation model combines template-based generation, pattern recognition, and response synthesis into a single integrated system that produces synthetic training data. This merging approach simplifies the architecture by consolidating multiple functions into one cohesive model that efficiently generates diverse training material.
Data Source
AI summary
System and methods for generating training data for fine tuning a model in a data scarce setting are disclosed. In some embodiments, a disclosed method includes: receiving, from a user interface, an indication of a customer's interaction with a website, the indication including a customer query, generating, using a first model, a plurality of synthetic queries, generating, using the first model, a plurality of synthetic responses to each of the plurality of synthetic queries, the plurality of synthetic responses being generated based on one or more rules and each synthetic response of the plurality of responses having a positive response and a negative response, aggregating, using a second model, the plurality of responses to generate training data, and training a third model using the training data, the third model configured to generate a response to the customer query in real-time.