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When evaluating constrained generation, one usually needs to measure both constraint compliance rate and language quality (such as perplexity and human ratings), and the two often involve a trade-off.
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10/28/2026
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UnverifiedWhen manually evaluating generation quality, without a standard test set, judgments are easily affected by sample selection, question phrasing, and evaluators' subjective expectations; a more rigorous approach is to construct a question set covering different difficulty levels and domains, have multiple evaluators score independently, and compare against public benchmarks or human-written answers.75% similarVerifiedThe validator focuses on whether the result satisfies mechanically verifiable constraints and makes a binary, objective judgment; the evaluator focuses on whether the output quality meets the goal and makes a subjective judgment at the semantic level.74% similarUnverifiedRigorous evaluation usually requires referencing results on public code benchmarks (such as HumanEval, SWE-bench, etc.) and conducting side-by-side comparisons under identical prompts and context conditions.73% similarUnverifiedRelative evaluation only requires task-level comparable signals (such as whether the task is completed, whether the tool call fails, whether the output passes validation), but its reliability depends heavily on the quality of task signals and the significance of candidate differences.73% similarUnverifiedIn model evaluation, the two types of metrics—"content correctness" and "format compliance"—should be distinguished, and stress testing should be conducted using multiple semantically equivalent variants.73% similar
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