The article discusses the limitations of standard evaluation methods for large language models (LLMs) used in public comment analysis, highlighting the importance of recognizing model disagreements as a sign of interpretive complexity. It proposes an Interpretive Audit Pipeline to enhance human review of ambiguous inputs, suggesting that disagreement-based evaluation should complement traditional accuracy metrics.