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English(EN) Foreword: Pre-Generation Prediction and Characterisation of LLM Test Failures using the Jacobian Lens

新框架“Foreword”利用雅可比透镜预测LLM测试失败

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个名为Foreword的新框架，以更有效地预测和表征大型语言模型（LLM）的测试失败。该框架利用“雅可比透镜”（J-lens）根据模型的内部状态来表示输入，并从成功的和失败的测试用例中学习。Foreword与逻辑回归或高斯混合模型等估计器配对时，在预测失败和优先排序各种任务和开源LLM的测试用例方面，其性能显著优于Clotho等现有方法。该方法还提供了对失败原因的语义洞察，例如任务规范中的歧义。 AI

影响 该框架可以显著降低LLM测试的成本并提高其效率，从而加快开发周期并带来更可靠的AI系统。

排序理由 该集群描述了一篇关于LLM测试的新研究论文，该论文详细介绍了一个新颖的框架和方法论。

在 Hugging Face Daily Papers 阅读 →

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新框架“Foreword”利用雅可比透镜预测LLM测试失败

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该集群描述了一篇关于LLM测试的新研究论文，该论文详细介绍了一个新颖的框架和方法论。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Somin Kim, Robert Feldt, Shin Yoo ·

    前言：使用雅可比透镜对大型语言模型测试失败进行预生成预测和特征化

    arXiv:2610.28187v1 Announce Type: cross Abstract: Testing LLM-based software is expensive: each input needs several sampled responses, each judged by a costly oracle. Further, every prompt or model change calls for re-testing. Pre-generation adequacy measures cut this cost by ran…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    前言：使用雅可比透镜对大型语言模型测试失败进行预生成预测和特征化

    Testing LLM-based software is expensive: each input needs several sampled responses, each judged by a costly oracle. Further, every prompt or model change calls for re-testing. Pre-generation adequacy measures cut this cost by ranking inputs by failure risk based on the model's i…

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