LLM Evals Often Miss Whether the Model Understood the Question

The article argues that current LLM evaluation frameworks are primarily output-centric, focusing on correctness, relevance, groundedness, safety, and style after the model has already answered. It asserts that this approach misses a critical layer: whether the model understood the task before it started. The author contends that a fluent answer does not necessarily equate to understanding, and models can produce confident responses based on misread prompts or incomplete context. To address this, the article proposes adding a 'comprehension_score' to LLM evaluations, a self-assessed estimate of how well the model understands the request before answering. This score would not replace existing evaluations but would add a missing layer between prompt ingestion and final output. The proposed scale is asymptotic and nonlinear, with different score ranges corresponding to different model behaviors, such as executing fully, executing with noted assumptions, or asking clarifying questions. The author emphasizes that the comprehension score is a dialogue tool, not a model grade, and can help separate understanding from guessing and reduce sycophancy. The article concludes by noting that the proposal is a practical one, built from real sessions, tested operationally, and confirmed across models.

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