Persona-Based ML Model Card Commentary Generation
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Solution Overview
Problem
Existing machine learning model cards lack the ability to automatically generate comprehensive, accurate, and understandable commentary data without manual intervention, particularly for non-expert personas, and struggle with processing complex, high-dimensional, and unstructured metadata.
Innovation Solution
A system and method utilizing a data curator ML model and a commentary generator ML model to preprocess metadata, generate persona-based report trajectories, and create prompt templates, enabling the generation of commentary data tailored to specific personas, including structured and unstructured metadata processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing machine learning techniques are used to generate model cards, then the model cards can be created with basic information, but they lack comprehensive and understandable commentary data for non-expert personas
Solution Approach 1:
The system generates different commentary data for different personas (e.g., executives, data scientists, end users) by retrieving persona-specific metadata and generating tailored commentaries. Each persona receives customized commentary that matches their expertise level and information needs, rather than a generic one-size-fits-all approach.
Solution Approach 2:
The commentary generator ML model acts as an intermediary that transforms complex, high-dimensional metadata into simplified, persona-appropriate commentary text. This intermediary process makes technical model information accessible to non-experts while preserving accuracy for experts.
2Reliability
If manual intervention is used to annotate and label training data, then data quality can be ensured, but the process requires significant time and human resources
Solution Approach 1:
The system uses ML models (data curator model and commentary generator) to automatically process, curate, and generate commentary data from metadata without requiring manual human intervention. The models self-service the task of creating high-quality commentary data that would traditionally require expert annotators.
Solution Approach 2:
The system performs preliminary processing of metadata to extract relevant information before generating final commentary. The data curator model pre-processes metadata to identify key facts, metrics, and characteristics, which then feed into the commentary generation process, ensuring quality without manual intervention at the final stage.
3Loss of information
If complex high-dimensional unstructured data is represented in model cards, then comprehensive information is provided, but it becomes difficult to understand for non-expert personas
Solution Approach 1:
The system changes the parameters of information presentation by adjusting the complexity, terminology, and detail level of commentary based on the target persona. The same underlying metadata is transformed into different commentary styles - simplified for executives, technical for data scientists, and user-focused for end users.
Solution Approach 2:
The model card commentary is segmented into different sections tailored to different personas. Each persona receives a customized subset of commentary that focuses on information relevant to their role, making the overall complex information manageable and understandable for each user group.
4Measurement precision
If prompt engineering is used to interact with LLMs, then accurate responses can be obtained, but it requires user feedback and prompt iteration which increases time consumption
Solution Approach 1:
The system performs preliminary prompt engineering by creating optimized prompt templates beforehand based on persona characteristics and information needs. These pre-engineered prompts are then directly used without requiring iterative refinement during actual model card generation, maintaining accuracy while reducing time consumption.
Data Source
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AI summary
This disclosure relates generally to method and system to generate persona-based commentary data for machine learning model card document. Existing techniques on model card are designed mainly for personas and understanding section of the model card document requires a certain level of expertise in machine learning. The method of the present disclosure receives a model card document comprising a plurality of sections and the model card document corresponds to a persona. Each section of the model card document obtains a metadata for the persona. The data curator machine learning model automatically generates a persona-based report trajectory for a plurality of sections of the metadata and a plurality of schema rules to generate a prompt template. The commentary generator ML model generates one or more commentary data for each section associated with the prompt template corresponding to the persona.