BERT embeddings and meta-classifiers trained on 172 expert-annotated defeaters from two assurance cases achieve F1≈0.84 in predicting quality ratings, outperforming the low inter-rater agreement (κ<0.442) between human reviewers.
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Automating Quality Assessment with NLP of LLM-Generated Defeaters
BERT embeddings and meta-classifiers trained on 172 expert-annotated defeaters from two assurance cases achieve F1≈0.84 in predicting quality ratings, outperforming the low inter-rater agreement (κ<0.442) between human reviewers.