Automated Prompt Engineering for Language Model Alignment Gaps
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Solution Overview
Problem
Large language models suffer from hallucination, leading to nonsensical, wrong, or offensive outputs, making them unreliable in applications requiring high accuracy, and human evaluations are impractical at an industrial scale.
Innovation Solution
A system and method that uses a judge language model to analyze and enhance prompts for a test language model, iteratively refining them to align with a metric by identifying misalignments and causes, thereby improving output accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human evaluations are used to assess language model output alignment, then evaluation accuracy is improved, but productivity deteriorates due to impossibility of scaling to industrial production levels
Solution Approach 1:
The patent creates a judge language model that copies human evaluation capabilities through automated means. The judge model is trained on human evaluation data and replicates human judgment patterns, allowing industrial-scale automation while maintaining evaluation quality. This is evident in the automated evaluation system that processes numerous model outputs without human intervention.
Solution Approach 2:
The language model evaluates itself through the judge model mechanism. The system enables self-assessment where the model's own outputs are automatically evaluated by the judge model against alignment criteria, eliminating the need for external human evaluators while maintaining continuous improvement through automated feedback loops.
2Reliability
If reasoning language models are used to provide explanations for outputs, then reliability is improved by reducing hallucination, but device complexity worsens due to additional computational layers
Solution Approach 1:
The patent segments the language model system into distinct functional components: the test model that generates outputs and the separate judge model that evaluates alignment. This segmentation allows each component to specialize in its function while working together through structured interaction protocols, managing complexity through modular design.
Solution Approach 2:
The judge model serves as an intermediary between the test model's outputs and the final evaluation results. It mediates the alignment assessment by translating model outputs into structured evaluations against alignment criteria, providing a buffer that simplifies the overall system architecture while maintaining reliability.
3Productivity
If automated evaluation systems are implemented, then productivity is improved by enabling industrial-scale processing, but measurement precision worsens due to difficulty in emulating human judgment
Solution Approach 1:
The patent implements a feedback mechanism where the judge model's evaluations are used to generate alignment scores that inform subsequent model iterations. This feedback loop continuously refines the evaluation process, allowing the automated system to improve its measurement precision over time while maintaining high productivity through automation.
Solution Approach 2:
The system performs preliminary actions by training the judge model on human evaluation data before deployment. This preliminary training phase establishes the foundation for accurate automated evaluation, ensuring that when the system processes industrial-scale data, it already possesses the judgment patterns necessary for precise alignment assessment.
Data Source
AI summary
An alignment score is generated for a test language model from input data including a number of triplet data structures. The method also includes identifying, responsive to the alignment score failing to satisfy a score threshold, a fail triplet data structure in the number of triplet data structures for which the evaluation score includes the indication of fail. A judge language model is executed on the fail triplet data structure to output a type of misalignment that the test language model produced when the test language model executed on the fail prompt. The judge language model is re-executed on a combination of the fail triplet data structure and the type of misalignment to output a cause of the fail response. An enhanced prompt is generated accordingly and then returned.