Accuracy is Not Agreement: Expert-Aligned Evaluation of Crash Narrative Classification Models

• URL: http://arxiv.org/abs/2504.13068v1
• Date: Thu, 17 Apr 2025 16:29:08 GMT
• Title: Accuracy is Not Agreement: Expert-Aligned Evaluation of Crash Narrative Classification Models
• Authors: Sudesh Ramesh Bhagat, Ibne Farabi Shihab, Anuj Sharma,
• Abstract summary: This study explores the relationship between deep learning (DL) model accuracy and expert agreement in the classification of crash narratives.<n>We evaluate five DL models -- including BERT variants and the Universal Sentence (USE) -- against expert-labeled data and narrative text.<n>Findings indicate that expert-aligned models tend to rely more on contextual and temporal language cues, rather than location-specific keywords.
• Score: 2.1797343876622097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study explores the relationship between deep learning (DL) model accuracy and expert agreement in the classification of crash narratives. We evaluate five DL models -- including BERT variants, the Universal Sentence Encoder (USE), and a zero-shot classifier -- against expert-labeled data and narrative text. The analysis is further extended to four large language models (LLMs): GPT-4, LLaMA 3, Qwen, and Claude. Our results reveal a counterintuitive trend: models with higher technical accuracy often exhibit lower agreement with domain experts, whereas LLMs demonstrate greater expert alignment despite relatively lower accuracy scores. To quantify and interpret model-expert agreement, we employ Cohen's Kappa, Principal Component Analysis (PCA), and SHAP-based explainability techniques. Findings indicate that expert-aligned models tend to rely more on contextual and temporal language cues, rather than location-specific keywords. These results underscore that accuracy alone is insufficient for evaluating models in safety-critical NLP applications. We advocate for incorporating expert agreement as a complementary metric in model evaluation frameworks and highlight the promise of LLMs as interpretable, scalable tools for crash analysis pipelines.

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