LLM-Generated User Behavior Surrogates for Long-Term Metric Prediction
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Solution Overview
Problem
Traditional online system experimentation methods for improving user experiences are time-consuming, computationally costly, and resource-intensive, leading to extended wait times before analyzing the effects of changes implemented in online platforms.
Innovation Solution
An online system utilizes large language models (LLMs) to simulate user behavior, generating surrogate metrics that predict long-term user interactions, thereby reducing the need for extensive real-world experiments and shortening the analysis time for change implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional A/B testing and real-world feedback methods are used to evaluate platform changes, then measurement precision is improved, but loss of time and productivity deteriorate due to extended experiment durations and computational costs
Solution Approach 1:
The system performs preliminary actions by training the LLM model beforehand on historical user interaction data. This pre-training enables the model to predict user responses to treatment variations without requiring actual real-time experiments, thus resolving the contradiction by preparing the predictive capability in advance while avoiding time-consuming live tests
Solution Approach 2:
The system creates synthetic copies of user responses through LLM-generated predictions instead of relying solely on real-world user interactions. These copied responses simulate actual user behavior patterns, allowing the system to evaluate platform changes with high measurement precision while avoiding the time delays associated with waiting for real user feedback
2Reliability
If large-scale real-world experiments are conducted to test changes over extended time periods, then reliability of results is improved, but use of energy and computational resources worsen
Solution Approach 1:
The system extracts the essential predictive signal from historical data through LLM training, separating the core user response patterns from the noisy and resource-intensive process of collecting new real-world experimental data. This extraction allows reliable predictions to be made without investing additional computational energy into large-scale experiments
Solution Approach 2:
The system changes the parameter of data source from real-time user interactions to LLM-generated predictions. This parameter change maintains reliability by using the LLM's learned understanding of user behavior while dramatically reducing the computational energy required, as generating predictions is far less resource-intensive than orchestrating and processing large-scale real-world experiments
3Measurement precision
If traditional experimentation methods are used to evaluate platform changes, then measurement precision is improved, but productivity deteriorates due to extended wait times before analysis
Solution Approach 1:
The LLM model serves as an intermediary between platform changes and evaluation. Instead of directly observing real user responses (which takes time), the system uses the LLM intermediary to predict outcomes, maintaining measurement precision through the model's learned patterns while dramatically improving productivity by eliminating wait times for real-world experimentation
Data Source
AI summary
A method for predicting customer long-term behavior using LLM-based modeling is described. The online system receives a representation of a stimulus or treatment that is presented to a user and generates a summary of a simulated user profile. The online system performs an inference task in conjunction with the model serving system or interface system to infer one or more actions that will likely be performed in response to the representation of the stimulus based on the simulated user profile. The online system computes a surrogate measure based on the response received from the model serving system and computes a correlation coefficient between the surrogate measure and a true metric of interest from collected experiment data. Responsive to determining a correlation coefficient greater than a threshold value, the online system predicts the true metric of interest based on the surrogate measure.