Language Model Validation via Probability Filtering
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Solution Overview
Problem
Language models struggle to adapt to evolving languages due to inaccessible user data, which complicates analysis and validation, especially when user privacy preserving training processes introduce undesirable biases or deviations.
Innovation Solution
An electronic device processes tokens predicted by a language model trained using a user privacy preserving process, determining the probability of predictions and selectively outputting token sequences within a predetermined range to identify and address undesirable predictions without accessing user data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user data is used to train the language model to keep it up-to-date with evolving language, then the language model becomes more relevant and accurate, but user privacy is compromised and data inaccessibility issues arise
Solution Approach 1:
A third-party validation system acts as an intermediary between the language model and user data. The validation system receives predictions from the model, validates them against privacy requirements and desired behavior, and provides feedback for adjustments without the model administrator directly accessing user data. This mediator enables accurate predictions while preserving privacy by decoupling the validation function from direct data access.
2Adaptability or versatility
If actual user data is used to complement the static training corpus, then the language model produces more relevant up-to-date predictions, but undesirable deviations and biases are introduced
Solution Approach 1:
The validation system implements a feedback mechanism that monitors model predictions for biases and undesirable deviations. When problematic predictions are detected (such as gender biases), the system generates feedback signals that trigger retraining or adjustment processes. This feedback loop enables the model to adapt to language evolution while continuously correcting harmful biases through iterative validation and refinement.
Solution Approach 2:
The validation system performs preliminary checks on predictions before they are output, identifying and blocking biased or harmful predictions in advance. By validating predictions against established criteria for acceptable behavior, the system prevents undesirable deviations from reaching the user, effectively countering potential biases before they can cause harm.
3Productivity
If the language model is updated to promote prediction of frequently used expressions, then prediction relevance improves, but the ability to maintain ethical standards and prevent biases becomes more difficult
Solution Approach 1:
The validation system performs preliminary validation of predictions against ethical standards and bias criteria before they are finalized. By checking predictions against predetermined ethical guidelines and desired behavior patterns in advance, the system ensures that high-productivity predictions still meet reliability and ethical requirements. This preliminary action maintains ethical consistency even as the model adapts to frequently used expressions.
Data Source
AI summary
Systems and methods for analysis and validation of language models trained using data that is unavailable or inaccessible are provided. One example method includes, at an electronic device with one or more processors and memory, obtaining a first set of data corresponding to one or more tokens predicted based on one or more previous tokens. The method determines a probability that the first set of data corresponds to a prediction generated by a first language model trained using a user privacy preserving training process. In accordance with a determination that the probability is within a predetermined range, the method determines that the one or more tokens correspond to a prediction associated with the user privacy preserving training process and outputs a predicted token sequence including the one or more tokens and the one or more previous tokens.