Hierarchical LLM Auto Evaluation for Custom Metrics at Scale
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing auto evaluation tools for large language models (LLMs) are limited in their applicability and require substantial modifications to accommodate custom evaluation patterns or novel tasks, often necessitating manual evaluation which is expensive and does not scale well.
Innovation Solution
A hierarchical auto evaluation system with a base interface that encapsulates core functionalities, allowing extension to create child classes tailored to specific evaluation metrics or families of metrics, enabling flexible and scalable evaluation of LLMs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation is used to ensure accurate assessment of LLMs, then evaluation precision is improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent creates a virtual copy of the evaluation process by training a student LLM to replicate the evaluation behavior of expert evaluators. Instead of manually evaluating each LLM output, the student LLM generates evaluation results that mimic human expert assessments, thereby reducing time consumption while maintaining evaluation precision through behavioral cloning rather than direct human intervention
Solution Approach 2:
The system enables LLMs to evaluate themselves through the student LLM framework. The student LLM, trained on evaluation patterns, allows LLMs to perform self-assessment and mutual evaluation, eliminating the need for continuous manual evaluation while preserving assessment quality through the learned evaluation capabilities embedded in the student model
2Productivity
If existing auto evaluation tools are used to reduce cost and time, then productivity is improved, but adaptability to custom evaluation patterns deteriorates
Solution Approach 1:
The patent implements a dynamic evaluation system where the student LLM can be retrained and adapted to different evaluation patterns and domains. Unlike static existing tools that require substantial modifications, the student LLM framework allows flexible adaptation by simply providing new training data and evaluation prompts, enabling the system to dynamically adjust to custom evaluation requirements while maintaining high evaluation efficiency
Solution Approach 2:
The student LLM serves as a universal evaluation engine that can handle multiple evaluation tasks across different domains and patterns. By training the student LLM on diverse evaluation datasets and patterns, the system achieves multi-functionality where a single framework can adapt to various custom evaluation scenarios without requiring separate tools or extensive modifications for each task
3Device complexity
If static evaluation tools are used to simplify implementation, then device complexity is reduced, but adaptability to novel tasks deteriorates
Solution Approach 1:
The system transitions from static evaluation tools to a dynamic student LLM framework that can learn and adapt to novel tasks. The student LLM is trained on evaluation patterns and can be continuously updated with new task types and domains, allowing the system to maintain simple implementation through a unified framework while achieving high adaptability to emerging evaluation scenarios through its learning capability
Data Source
AI summary
An auto evaluation system for evaluating large language models (LLMs). The auto evaluation system loads a base auto evaluation class with core functionalities, selects one or more metrics for evaluation, extends the base auto evaluation class to create a child class with additional functionalities tailored to the selected metrics. A judge LLM receives the evaluation prompts from the auto evaluation server for response generation and computes evaluation scores for the test LLM.