LLM Output Evaluation Functions for Comparative Response Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Evaluating the textual output of Large-Language Models (LLMs) is technically difficult due to their generative nature, making it challenging to compare performance across different models based on criteria such as style, syntax, content, and user preferences, unlike traditional machine learning models where evaluation can be done using clear objective metrics.
Innovation Solution
A data processing service with a control layer and data layer that includes a machine learning module to receive prompts, apply evaluation functions to LLM responses, and generate user interface elements highlighting identified words for easy comparison, allowing users to evaluate and compare LLM outputs effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional objective metrics are used to evaluate LLM outputs, then evaluation simplicity is maintained, but evaluation accuracy deteriorates due to the generative nature of LLM outputs
Solution Approach 1:
The patent introduces an intermediary evaluation system that uses a second LLM (evaluation model) as a mediator to assess the outputs of the first LLM. This intermediary layer enables complex, nuanced evaluation of generative outputs while maintaining a structured evaluation process. The evaluation model acts as a mediator between the prompt and the generated response, applying multiple evaluation criteria to provide comprehensive feedback.
2Measurement precision
If multiple evaluation criteria are applied to LLM outputs, then evaluation comprehensiveness is improved, but evaluation complexity increases
Solution Approach 1:
The patent segments the evaluation process into distinct evaluation criteria or dimensions (e.g., relevance, coherence, creativity, toxicity). Each criterion can be evaluated independently using specialized evaluation functions, making the overall complex evaluation task manageable and systematic. This segmentation allows the evaluation model to focus on one aspect at a time while maintaining comprehensive coverage.
Solution Approach 2:
The evaluation system is designed to be multi-functional, capable of applying multiple evaluation criteria simultaneously through a unified evaluation framework. The same evaluation model can assess different aspects of LLM outputs (quality, safety, relevance) using different evaluation functions, reducing the need for separate specialized systems for each criterion.
3Adaptability or versatility
If LLM outputs are evaluated manually, then evaluation flexibility is maintained, but evaluation time increases
Solution Approach 1:
The patent implements self-service evaluation where the evaluation model automatically assesses LLM outputs without requiring manual human intervention. The system performs self-evaluation by applying evaluation functions to the generated responses, providing immediate feedback and reducing evaluation time significantly compared to manual review processes.
Data Source
AI summary
A method for evaluating textual output of one or more machine-learned language models is presented. The method includes receiving, from a user of a client device, a first prompt for input to one or more machine-learned language models, providing the first prompt to the one or more models for execution, and receiving a set of generated responses to the first prompt from the one or more models. The method further includes generating a user interface (UI) on the client device displaying the first prompt and generated responses as a table user interface element. The method applies a selected evaluation function to the generated response to evaluate the response with respect to an evaluation objective and identifies words that influence the evaluation. The method generates one or more UI elements on the UI to display the results of the evaluation for the generated responses.