Human-Calibrated Automated Testing and Validation of Generative Language Models

• URL: http://arxiv.org/abs/2411.16391v2
• Date: Sat, 07 Dec 2024 16:12:38 GMT
• Title: Human-Calibrated Automated Testing and Validation of Generative Language Models
• Authors: Agus Sudjianto, Aijun Zhang, Srinivas Neppalli, Tarun Joshi, Michal Malohlava,
• Abstract summary: This paper introduces a comprehensive framework for the evaluation and validation of generative language models (GLMs) It focuses on Retrieval-Augmented Generation (RAG) systems deployed in high-stakes domains such as banking.
• Score: 3.2855317710497633
• License:
• Abstract: This paper introduces a comprehensive framework for the evaluation and validation of generative language models (GLMs), with a focus on Retrieval-Augmented Generation (RAG) systems deployed in high-stakes domains such as banking. GLM evaluation is challenging due to open-ended outputs and subjective quality assessments. Leveraging the structured nature of RAG systems, where generated responses are grounded in a predefined document collection, we propose the Human-Calibrated Automated Testing (HCAT) framework. HCAT integrates a) automated test generation using stratified sampling, b) embedding-based metrics for explainable assessment of functionality, risk and safety attributes, and c) a two-stage calibration approach that aligns machine-generated evaluations with human judgments through probability calibration and conformal prediction. In addition, the framework includes robustness testing to evaluate model performance against adversarial, out-of-distribution, and varied input conditions, as well as targeted weakness identification using marginal and bivariate analysis to pinpoint specific areas for improvement. This human-calibrated, multi-layered evaluation framework offers a scalable, transparent, and interpretable approach to GLM assessment, providing a practical and reliable solution for deploying GLMs in applications where accuracy, transparency, and regulatory compliance are paramount.

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