Surrogate Evaluation Models for Generative QA Performance
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Solution Overview
Problem
Evaluating the accuracy of generative question-answering systems in AI is challenging due to the reliance on expensive human expert validations and costly LLM API calls for each answer, leading to high computational and network resource usage.
Innovation Solution
A surrogate machine learning model is trained using a dataset of prior question-answer pairs to generate evaluation scores, reducing the need for additional LLM calls and improving processing efficiency by learning how to assess answer quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human expert validations are used to evaluate question-answering systems, then measurement precision is improved, but use of energy and loss of time increase significantly
Solution Approach 1:
The patent creates a surrogate evaluation model that copies the evaluation capabilities of human experts. This surrogate model is trained on previously evaluated question-answer pairs and can replicate expert judgment without requiring actual human experts to perform manual evaluations, thereby reducing computational and temporal resources while maintaining evaluation accuracy.
Solution Approach 2:
The system performs preliminary evaluation of question-answer pairs using the surrogate model before final human expert validation. This preliminary action filters and prioritizes cases that need human review, allowing human experts to focus only on complex or ambiguous evaluations, thus reducing overall computational resource consumption while maintaining high measurement precision.
2Measurement precision
If LLM API calls are made for each question-answer pair evaluation, then measurement precision is improved, but loss of time and use of energy increase
Solution Approach 1:
The patent trains a surrogate evaluation model that learns from previous LLM evaluations and human validations. This surrogate model can quickly assess question-answer pairs without requiring real-time LLM API calls, significantly reducing processing time while maintaining evaluation accuracy through its trained knowledge of effective evaluation criteria.
Solution Approach 2:
The system performs preliminary training of the evaluation model using historical data before actual evaluations are needed. This preliminary action enables the system to make rapid evaluations during production without repeatedly calling LLM APIs, thus reducing time loss while preserving measurement precision through the pre-trained model's sophisticated evaluation capabilities.
3Reliability
If expensive human expert validations are used, then reliability is improved, but device complexity and loss of time increase
Solution Approach 1:
The patent implements a surrogate evaluation model that copies the reliable judgment patterns of human experts. This surrogate model is trained on high-quality labeled data from expert evaluations, enabling it to provide consistent and trustworthy assessments without requiring the actual human experts to be continuously available, thus reducing system complexity while maintaining reliability.
Solution Approach 2:
The surrogate evaluation model acts as an intermediary between the question-answering system and human experts. It handles routine evaluations autonomously, providing reliable assessments for most cases, while only escalating complex cases to human experts for final validation. This intermediary approach maintains system reliability while significantly reducing the complexity of managing human expert resources.
4Measurement precision
If multiple LLM API calls are made for evaluation, then measurement precision is improved, but use of energy and productivity decrease
Solution Approach 1:
The patent trains a surrogate evaluation model that replicates the precision of multiple LLM evaluations but executes much faster. The surrogate model is trained on the intricate patterns and criteria that enable accurate evaluation, allowing it to assess question-answer pairs with high precision in a fraction of the time required for multiple LLM API calls, thus dramatically improving productivity without sacrificing measurement precision.
Data Source
AI summary
Systems and methods are disclosed herein for evaluating the performance of a question-answering model. In an example system, a set of prior question-answer pairs is obtained. In an example, each prior question-answer pair comprising a question and an associated answer that was generated previously. Each prior question-answer pair is provided to a LLM to obtain an evaluation score for the prior question-answer pair. In an embodiment, the evaluation score contains a value indicative of a quality of the answer to the question. An evaluation model is trained using features and labels, where the features are based on each prior question-answer pair and the labels are based on the evaluation score for each prior question-answer pair. When a current question-answer pair is obtained (e.g., for evaluation), the evaluation model is applied to the current question-answer pair to generate an evaluation score.