What are evaluation challenges in NLP models beyond accuracy?

NLP evaluation is difficult because meaning is subjective, context-dependent, and multi-dimensional.

Traditional metrics like accuracy or BLEU fail to capture semantic correctness, fluency, and factual consistency. Modern evaluation uses human judgment, embedding-based similarity, adversarial testing, and LLM-as-a-judge approaches. Bias and hallucination detection further complicate evaluation.