AI Preference Model for Reliable Generative Model Selection
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Solution Overview
Problem
Generative AI models face challenges in reliability due to uncertainty, ambiguity, lack of ground truth, variability, and ethical concerns, leading to complex and time-consuming response selection processes, especially in critical scenarios.
Innovation Solution
A collaborative AI preference model that includes an intent model to classify user inputs, a preference model to select the most suitable generative AI model based on user feedback, and a sentiment model to update preferences, ensuring accurate and reliable responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users query multiple generative AI models to obtain a suitable response, then response reliability is improved, but complexity and time overhead increase
Solution Approach 1:
The patent introduces a preference model as an intermediary component that mediates between multiple generative AI models and the user. This preference model learns user preferences and model characteristics to automatically select the most suitable model for each query, eliminating the need for users to manually query multiple models while maintaining response reliability.
Solution Approach 2:
The system implements feedback mechanisms where user responses and interactions are continuously fed back to update the preference model. This allows the system to learn from past interactions and improve its model selection accuracy over time, ensuring reliable responses without increasing user burden.
2Reliability
If users query multiple generative AI models to obtain a suitable response, then response reliability is improved, but time overhead increases
Solution Approach 1:
The preference model performs preliminary learning and analysis of multiple generative AI models' characteristics and performance during training phases. This preliminary action enables the model to make rapid, informed selections for new queries without requiring users to wait for multiple model responses, thus maintaining reliability while reducing time overhead.
3Adaptability or versatility
If generative AI models operate probabilistically, then flexibility and adaptability are improved, but certainty and accuracy decrease
Solution Approach 1:
The preference model acts as an intermediary that selects the most appropriate probabilistic model for each specific query based on learned patterns and user preferences. This mediation allows the system to maintain the flexibility and adaptability of probabilistic operations while improving certainty and accuracy by choosing the best-suited model for each context.
4Reliability
If different generative AI models are used for different contexts, then response quality is improved, but model selection complexity increases
Solution Approach 1:
The preference model enables self-service automatic model selection by learning to match query characteristics with appropriate generative AI models. This eliminates the need for complex manual selection processes or user expertise, allowing the system to automatically provide high-quality responses while keeping the selection mechanism simple and transparent.
Data Source
AI summary
It is a challenge to ensure the reliability of generative artificial intelligence (AI), due to a number of factors, including uncertainty, ambiguity, the absence of ground truth, variability among models, ethical implications, and the like. Accordingly, embodiments implement a chatbot that is capable of determining a user's intent, uses a preference model to select one of a plurality of generative AI models that is best suited for that intent, and responds using the selected generative AI model. In addition, the chatbot may capture users' sentiments in their replies and update the preference model accordingly, for continual improvement in the selection of the generative AI models using reinforcement learning from human feedback. The preference model may also account for other metrics of each generative AI model, such as performance, utility, and ethics.