Generative artificial intelligence (AI) applications powered by large language models (LLMs) are revolutionizing question-answering use cases. These applications provide human-like responses to natural language queries, making them essential for various tasks like customer support and conversational AI assistants. However, deploying responsible AI best practices requires a robust ground truth and evaluation framework to ensure quality and user experience standards are met.
In this post, we delve into the evaluation and interpretation of metrics using FMEval for question answering in a generative AI application. FMEval, a comprehensive evaluation suite from Amazon SageMaker Clarify, offers standardized metrics to assess quality and responsibility.
The post provides insights on best practices for using FMEval in ground truth curation and metric interpretation for evaluating question answering applications. Ground truth data in AI refers to known true data that acts as a reference point for evaluating system quality. By following these guidelines, data scientists can quantify the user experience provided by generative AI pipelines and communicate effectively with business decision-makers.
Solution Overview
Example ground truth data (referred to as the golden dataset) of 10 question-answer-fact triplets derived from Amazon’s Q2 2023 10Q report is used for evaluating question answering applications. Responses from three generative AI RAG pipelines are generated and factual knowledge and QA accuracy metrics are calculated against the golden dataset.
Evaluation for Question Answering in a Generative AI Application
Generative AI pipelines consist of components like RAG (Retrieval Augmented Generation), which enhance the accuracy of LLM responses by retrieving and inserting domain knowledge into the language model prompt. Tuning parameters in the retriever and generator components ensures the correct content is available for generation.
Question answering can also be powered by fine-tuned LLMs or agentic approaches. Evaluating final responses from generative AI pipelines helps in making informed decisions, comparing different pipeline architectures, and ensuring compliance with standards like ISO42001 AI Ethics.
The evaluation process involves querying the generative AI pipeline, evaluating responses against the golden dataset, interpreting scores, and making data-driven business decisions.
FMEval Metrics for Question Answering in a Generative AI Application
Factual Knowledge
The Factual Knowledge metric evaluates whether the generated response contains factual information present in the ground truth answer. It provides a binary score based on exact string matching, aiding in assessing the factual correctness of generative AI pipelines.
QA Accuracy
The QA Accuracy metric measures a model’s question-answering accuracy by comparing generated answers against ground truth. It includes sub-metrics like Recall Over Words, Precision Over Words, and F1 Over Words to evaluate the model’s performance.
Proposed Ground Truth Curation Best Practices for Question Answering with FMEval
Understanding Factual Knowledge Metric Calculation
Ground truth curation for Factual Knowledge involves providing minimal fact representations and using logical operators to handle multiple acceptable answers.
Interpreting Factual Knowledge Scores
Factual Knowledge scores help in identifying challenges in generative AI pipelines, such as hallucination or information retrieval issues.
Curating Factual Knowledge Ground Truth
Curate ground truth facts representing the most important facts and generate multiple variants to accommodate different units and formats.
Understanding QA Accuracy Metric Calculation
QA Accuracy metrics are calculated based on true positive, false positive, and false negative matches between model responses and ground truth, providing insights on model performance.
Interpreting QA Accuracy Scores
Balance recall, precision, and F1 scores to assess model performance effectively and ensure conciseness and accuracy in responses.
Curating QA Accuracy Ground Truth
Ensure ground truth answers reflect the desired user experience and use LLMs to automate initial ground truth generation, followed by human review for alignment with production standards.
Conclusion
Effective ground truth curation and metric interpretation play a vital role in deploying generative AI question answering pipelines. By following best practices and utilizing FMEval, businesses can make informed decisions and ensure the quality and responsibility of their AI systems.