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English(EN) Self-Verification Reported 99.3% Precision While Delivering 73.2%. Precision Was the Wrong Metric

LLM 中的自验证:精度指标具有误导性,覆盖率至关重要

一位开发者衡量了 LLM 中自验证的有效性,发现虽然它可以提高精度,但通常会以降低整体交付率为代价。确定的关键问题是将精度用作指标的误用,因为在应用过滤时,精度在结构上不可能降低。更准确的评估需要同时考虑精度和覆盖率,这表明一个有缺陷的验证器会拒绝正确的答案,从而在某些情况下导致整体性能下降。 AI

影响 强调了在评估 LLM 技术(如自验证)时准确选择指标的重要性。

排序理由 该条目是一篇观点文章,是对一项技术的衡量,而不是主要发布或研究论文。

在 dev.to — LLM tag 阅读 →

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LLM 中的自验证:精度指标具有误导性,覆盖率至关重要

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报道来源 [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Devanshu Biswas ·

    自验证报告称精度达到99.3%,但实际精度为73.2%。精度是错误的衡量标准

    <p>"Ask the model to check its own answer" is one of those techniques everybody repeats and nobody measures. So I measured it, on 4,000 generated problems where the ground truth is known by construction.</p> <p>The first version of my own page got the measurement wrong, and the w…