首个语音理解场景的模型删忆评测基准，验证删除特定说话人数据的能力。
"Alexa, can you forget me?" Machine Unlearning Benchmark in Spoken Language Understanding
- 构建多语言语音理解数据集上的删忆评测框架
- 8种方法中部分能有效删除指定说话人信息但效率差异大
- 适合关注隐私保护与模型可解释性的研究者
机器删忆是指高效移除机器学习模型中的特定信息，是负责任AI的重要方向。然而，现有研究较少涉及复杂任务，尤其是语音相关任务的删忆效果评估。本文提出UnSLU-BENCH，首个面向语音理解（SLU）的机器删忆基准，涵盖四种语言的四个数据集。以删除特定说话人数据为场景，评估潜在“被遗忘权”请求的有效性。我们评估了八种删忆技术，并提出一种新指标，同时衡量其有效性、实用性和效率。该基准为语音理解中的删忆研究奠定基础，揭示不同方法在效果和计算可行性上的显著差异。
原文摘要 · Abstract (English)
Machine unlearning, the process of efficiently removing specific information from machine learning models, is a growing area of interest for responsible AI. However, few studies have explored the effectiveness of unlearning methods on complex tasks, particularly speech-related ones. This paper introduces UnSLU-BENCH, the first benchmark for machine unlearning in spoken language understanding (SLU), focusing on four datasets spanning four languages. We address the unlearning of data from specific speakers as a way to evaluate the quality of potential "right to be forgotten" requests. We assess eight unlearning techniques and propose a novel metric to simultaneously better capture their efficacy, utility, and efficiency. UnSLU-BENCH sets a foundation for unlearning in SLU and reveals significant differences in the effectiveness and computational feasibility of various techniques.
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