LLM Evaluation Workflow With Metric Selection and Sample Sizing
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Solution Overview
Problem
There is a lack of effective tools and metrics to gauge the accuracy of generative artificial intelligence (GenAI) outcomes, leading to potential deployment of unreliable models that can result in poor decisions and customer dissatisfaction.
Innovation Solution
A computer-implemented method for evaluating large language models (LLMs) that includes receiving user inputs, determining recommended metrics and prompts, augmenting datasets, and generating evaluation reports to assess accuracy, using a generative AI workbench with specialized tasks for objective and subjective evaluations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative AI models are deployed without proper evaluation tools and metrics, then deployment speed is improved, but reliability and accuracy of outcomes deteriorate
Solution Approach 1:
The patent implements preliminary evaluation actions by automatically generating evaluation datasets, selecting appropriate metrics, and assessing model performance before deployment. The system prepares evaluation frameworks in advance using user inputs and model specifications, ensuring reliability checks are completed prior to deployment decisions.
Solution Approach 2:
The patent introduces an intermediary evaluation system that mediates between the generative AI model and deployment. This intermediary layer automatically generates evaluation datasets, selects metrics, and provides assessment results, acting as a bridge that ensures reliability without directly modifying the core model or deployment process.
2Measurement precision
If comprehensive evaluation metrics and datasets are implemented, then accuracy assessment is improved, but system complexity and time consumption increase
Solution Approach 1:
The patent implements dynamic metric selection that adapts to user inputs and model specifications. The system dynamically generates evaluation datasets and selects appropriate metrics based on the specific use case, rather than using a fixed comprehensive set. This dynamic approach maintains measurement precision while reducing unnecessary complexity.
Solution Approach 2:
The patent changes parameters of the evaluation system by automatically adjusting dataset size, metric selection, and evaluation depth based on user inputs and model characteristics. This parameter optimization ensures sufficient accuracy assessment without implementing unnecessarily complex comprehensive evaluations for all cases.
3Reliability
If adequate sample sizes are ensured for evaluation, then statistical reliability is improved, but data processing time and resources increase
Solution Approach 1:
The patent performs preliminary determination of adequate sample sizes based on user inputs and evaluation requirements. The system calculates and prepares the necessary dataset size in advance, ensuring statistical reliability without over-processing. This preliminary planning optimizes the balance between sample size adequacy and processing time.
4Productivity
If automated evaluation systems are implemented, then operational efficiency is improved, but initial setup complexity and cost increase
Solution Approach 1:
The patent implements self-service automation where the evaluation system automatically generates datasets, selects metrics, and produces assessment results based on user inputs. The system serves itself by autonomously completing evaluation tasks without requiring complex manual configuration, thereby improving operational efficiency while managing setup complexity through automated workflows.
Data Source
AI summary
A computer-implemented method of assessing a large language model (LLM) includes receiving user inputs concerning the LLM including selected hyperparameters, a use case, at least one prompt, and examples. The user inputs are mapped with a glossary of metrics to determine recommended metrics and recommended prompts for the LLM. A minimum recommended sample size is determined based on the user inputs, recommended at least one metric and expected confidence and accuracy. An LLM-generated dataset related to the use case is augmented when it is determined that the LLM-generated dataset has fewer entries than the minimum recommended sample size. An evaluation report is then generated for assessing the recommended at least one metric for determining the accuracy of: i) the LLM based on the user inputs, recommended at least one metric and the LLM-generated dataset, ii) the at least one user prompt, and iii) at least one recommended prompt.