Language Model Feedback Adaptation for Low-Latency Updates
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Solution Overview
Problem
Existing techniques for updating language models using user feedback are inefficient and cumbersome, particularly due to the scarcity of user data and the integration latency between action and token predictions, leading to increased cognitive burden and power consumption.
Innovation Solution
A method that integrates user feedback into the language model update process using an adversarial framework, adjusting the model based on user actions to converge to a target distribution efficiently, reducing latency and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If user feedback is integrated into language model updates using traditional methods, then the model can be updated with user data, but the process becomes cumbersome and inefficient due to data scarcity and integration latency
Solution Approach 1:
The system performs preliminary actions by generating predicted user actions in advance and preparing the adversarial framework before actual user feedback arrives. This allows the model to be pre-adjusted based on predicted behaviors, reducing the latency when actual feedback needs to be integrated.
Solution Approach 2:
The system implements a continuous feedback loop where user actions are detected, compared with predicted actions, and used to generate modified output token probability distributions. This feedback mechanism enables iterative refinement of the language model based on actual user behavior, improving update efficiency while maintaining adaptability.
2Adaptability or versatility
If both action predictions and token predictions are integrated, then more comprehensive user feedback is captured, but processing latency increases due to the difference in size and nature between the two prediction types
Solution Approach 1:
The system segments the feedback processing into two independent but coordinated streams: action prediction and token prediction. Each stream is processed separately through its own adversarial framework, allowing parallel processing that reduces overall latency while maintaining comprehensive feedback capture.
Solution Approach 2:
The system merges the results from action prediction and token prediction adversarial frameworks into a unified language model update process. This combination allows comprehensive feedback integration while using shared computational resources and coordinated updating to minimize processing latency.
3Measurement precision
If the language model is continuously updated with user data, then prediction accuracy for specific users improves, but power consumption increases in battery-operated devices
Solution Approach 1:
The system implements periodic updates rather than continuous real-time updates. The adversarial framework adjusts the language model at scheduled intervals or based on accumulated feedback thresholds, maintaining prediction accuracy while significantly reducing power consumption by keeping the processing model idle during non-update periods.
Solution Approach 2:
The system changes operational parameters by adjusting the frequency and intensity of model updates based on device state, feedback accumulation, and power availability. This allows the system to maintain accuracy when needed while conserving power during normal operation, dynamically optimizing the trade-off between precision and energy consumption.
Data Source
AI summary
The present disclosure generally relates to updating a language model based on user feedback. Based on a user text input, a language model predicts a set of tokens and an action that will be taken by the user in response to the predicted set of tokens. If the predicted action does not match a detected actual user action, the language model is updated to reflect the user feedback by modifying an output token probability distribution based on the actual user action and updating the language model to converge with a target language model using the modified output token probability distribution.