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English(EN) Influence Score and Transformers interpretability: Measure of the Effective Impact of Attention Heads at inference time

新的“影响分数”衡量Transformer注意力头的影响

研究人员开发了一种新的“影响分数”来衡量Transformer模型中注意力头的影响,特别是在分类任务中。该分数结合了对输出logits的方向性影响以及模型残差流中的结构贡献,从而可以在多个层面进行分析。当应用于用于提示注入检测的DeBERTa模型时,该框架突出了正确和错误预测之间决策过程的差异,提供了一种在详细电路分析和更广泛的基于输出的方法之间的平衡。 AI

影响 提供了一种理解和潜在改进基于Transformer的分类器决策过程的新方法。

排序理由 该集群包含一篇学术论文,详细介绍了分析Transformer模型的新研究方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的“影响分数”衡量Transformer注意力头的影响

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该集群包含一篇学术论文,详细介绍了分析Transformer模型的新研究方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Lisa Bouger, Yannick Teglia, Philippe Loubet Moundi ·

    影响得分与Transformer可解释性:衡量注意力头在推理时期的有效影响

    arXiv:2609.05074v1 Announce Type: new Abstract: We propose an influence score to quantify the contribution of attention heads to classification decisions in Transformer-based models designed for prompt injection detection. The score combines directional influence on the logits wi…