Automating Quality Assessment with NLP of LLM-Generated Defeaters

2025-07-10 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

An automated method assesses the quality of large language model (LLM)-generated "defeaters" for high-integrity system assurance cases. These defeaters challenge safety claims in systems like autonomous vehicles and large-scale energy infrastructures. Manual validation is subjective and unscalable, with human reviewers exhibiting low inter-rater agreement (Cohen's kappa κ<0.442). The new NLP-based approach integrates structural analysis of assurance case graphs with BERT embeddings and meta-classifiers, trained on expert-assessed consensus defeaters. Tested on automotive (Adaptive Cruise Control) and energy (CERN Large Hadron Collider) case studies, the method significantly improves consistency with individual human raters, boosting kappa by approximately 40%. It achieves an average F1-score of 0.84 across validation, providing a scalable and objective assessment that reduces subjective variance in assurance case synthesis.

Key takeaway

For AI Scientists and Machine Learning Engineers validating LLM-generated safety artifacts, this automated NLP method offers a path to overcome subjective manual reviews. You can achieve greater consistency and reduce inter-rater variability in assessing defeater quality for high-integrity systems. Consider integrating BERT embeddings and meta-classifiers with structural graph analysis to enhance the objectivity and scalability of your assurance case synthesis processes.

Key insights

Automating defeater quality assessment with NLP and graph analysis reduces subjectivity in safety assurance.

Principles

• Manual defeater validation is subjective and unscalable.
• Inter-rater agreement quantifies expert judgment variability.
• Objective metrics enhance assurance case reliability.

Method

The approach combines structural analysis of assurance case DAGs with BERT embeddings and meta-classifiers, trained on expert-assessed consensus defeaters, to objectively evaluate LLM-generated defeaters.

In practice

• Use BERT embeddings for semantic analysis.
• Apply meta-classifiers for refined predictions.
• Integrate structural context from assurance case graphs.

Topics

• Natural Language Processing
• Large Language Models
• Assurance Cases
• Safety-Critical Systems
• BERT Embeddings
• Defeater Validation

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.