Contextual Bandit Pre-Training With LLM-Generated Synthetic Users
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Solution Overview
Problem
Collecting detailed user data for initializing contextual multi-armed bandits is costly and raises privacy compliance issues, hindering effective initial performance.
Innovation Solution
Pre-train contextual multi-armed bandits using large language models (LLMs) to generate synthetic users and their interactions, creating a simulated dataset for initialization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If detailed user data is collected to initialize contextual multi-armed bandits, then initial performance is improved, but data gathering costs and privacy compliance issues increase
Solution Approach 1:
The patent uses synthetic users that copy the essential characteristics and behavioral patterns of real users without requiring actual user data collection. The synthetic users are generated with demographic features, preferences, and interaction patterns that mirror real user populations, enabling the contextual multi-armed bandit to be initialized with realistic data while avoiding privacy and cost issues associated with collecting real user data.
2Reliability
If detailed user data is collected to initialize contextual multi-armed bandits, then initial performance is improved, but privacy compliance issues increase
Solution Approach 1:
The patent replaces real user data with synthetic user data that copies the statistical properties and interaction patterns of real users. This approach maintains the effectiveness of personalization while completely avoiding privacy compliance issues since no actual user information is collected, stored, or processed.
Solution Approach 2:
The synthetic users act as an intermediary between the system requirements for user data and the privacy constraints. Instead of directly using real user data, the system uses synthetic data as a mediator that preserves the necessary behavioral patterns and preferences while eliminating all privacy and compliance concerns.
3Device complexity
If synthetic users are generated using LLMs, then data gathering costs are reduced, but computational resources for LLM processing increase
Solution Approach 1:
The patent performs the computationally intensive LLM processing in advance to generate synthetic users before the actual deployment of the contextual multi-armed bandit system. This preliminary action creates a reusable dataset of synthetic users that can be stored and used multiple times, amortizing the initial computational cost across many deployments and reducing ongoing operational costs.
Data Source
AI summary
Large language models (LLMs) are used to pre-train contextual multi-armed bandits. LLMs, which are trained on extensive corpora, preserve a repository representative of certain human behavior and preferences and can serve as a booster for training a contextual multi-armed bandit. An LLM is used to generate synthetic users and associated data, and then the LLM is used for simulated interactions of those synthetic users with the contextual multi-armed bandit. The resulting dataset is then used to pre-train the contextual multi-armed bandit.