LLM Evaluation Framework With Composite Input-Output Health Scoring
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Solution Overview
Problem
Existing evaluation frameworks for Large Language Models (LLMs) are inadequate in providing a comprehensive, holistic assessment that considers both input and output characteristics, failing to integrate diverse metrics effectively.
Innovation Solution
A method and system for end-to-end evaluation of LLMs using a combination of qualitative and quantitative metrics to assess input and output characteristics, including safety, toxicity, data quality, and response quality, with statistical techniques to derive a composite health score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive evaluation criteria are used to assess multiple dimensions of LLM performance, then the evaluation becomes more thorough and accurate, but the complexity of the evaluation process increases significantly
Solution Approach 1:
The evaluation framework is segmented into distinct modules, each responsible for assessing specific dimensions (language fluency, semantic coherence, contextual understanding, diversity, creativity, ethical considerations, performance robustness, and computational efficiency). This segmentation allows comprehensive evaluation while managing complexity through modular organization.
Solution Approach 2:
The framework transitions from single-dimension evaluation to multi-dimensional assessment by introducing additional evaluation axes. Each dimension represents a separate aspect of LLM performance, enabling comprehensive evaluation without overwhelming complexity by organizing dimensions systematically.
2Adaptability or versatility
If multiple evaluation dimensions are considered to achieve holistic understanding of LLM capabilities, then the assessment becomes more comprehensive, but the time required for evaluation increases
Solution Approach 1:
The evaluation process maintains continuous operation by concurrently assessing multiple dimensions without sequential delays. The framework enables parallel evaluation of different LLM capabilities, ensuring comprehensive assessment while minimizing total evaluation time through continuous processing.
Solution Approach 2:
Evaluation criteria and dimensions are pre-defined and organized in advance, allowing the actual evaluation to proceed efficiently without time-consuming setup. The framework structure is prepared beforehand, enabling rapid comprehensive assessment when executed.
3Reliability
If diverse evaluation metrics are integrated into a cohesive framework, then the assessment becomes more accurate and reliable, but the difficulty of integrating and coordinating these metrics increases
Solution Approach 1:
Multiple diverse evaluation metrics are merged into a unified framework that coordinates them systematically. The framework combines quantitative and qualitative metrics, as well as automated and human evaluation components, into a cohesive structure that maintains reliability while managing integration complexity.
Solution Approach 2:
The evaluation framework is designed with universal applicability across different LLM tasks and dimensions. The same framework structure can accommodate various metrics and evaluation types, reducing integration complexity through standardization while maintaining comprehensive and reliable assessment capabilities.
Data Source
AI summary
A method and a Large Language Model (LLM) evaluation system provides an end-to-end evaluation of LLM, which includes evaluating both input prompts and output prompt responses, wherein the evaluation includes assessing a plurality of input and output characteristics that encompasses both quality and quantity. Each of the plurality of input characteristics are assigned with a corresponding normalized score by employing one or more statistical techniques to derive a composite health score for the input prompts. Evaluation further comprises evaluating output prompt responses in both absence and presence of the ground truth. Upon evaluating both input prompts and output prompt responses, a final aggregated health score for the LLM is computed by a scorer module employing threshold based statistical techniques that considers input prompt health and output prompt response health, wherein the aggregated health score is generated based on the granular scores of each characteristic.