通过回顾模型解释来解释预测的不确定性
Explaining Predictive Uncertainty by Looking Back at Model Explanations
摘要：
预训练语言模型的预测不确定性估计是人们可以信任其预测的可能性的重要度量。然而，很少有人知道是什么使模型预测不确定。解释预测不确定性是解释预测标签的重要补充，可以帮助用户理解模型决策，获得用户对模型预测的信任，但在以往的研究中，这一问题被忽略了。在这项工作中，我们建议通过从现有的模型解释中提取不确定的单词来解释预训练的语言模型的预测不确定性。我们发现不确定的词是那些被认为对预测标签有负面贡献的词，而实际上解释了预测的不确定性。实验表明，不确定性解释对于解释模型和帮助人类理解模型预测行为是不可或缺的。
英文摘要：
Predictive uncertainty estimation of pre-trained language models is an important measure of how likely people can trust their predictions. However, little is known about what makes a model prediction uncertain. Explaining predictive uncertainty is an important complement to explaining prediction labels in helping users understand model decision making and gaining their trust on model predictions, while has been largely ignored in prior works. In this work, we propose to explain the predictive uncertainty of pre-trained language models by extracting uncertain words from existing model explanations. We find the uncertain words are those identified as making negative contributions to prediction labels, while actually explaining the predictive uncertainty. Experiments show that uncertainty explanations are indispensable to explaining models and helping humans understand model prediction behavior.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
