"Alexa, can you forget me?" Machine Unlearning Benchmark in Spoken Language Understanding
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912773848432640 |
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| author | Koudounas, Alkis Savelli, Claudio Giobergia, Flavio Baralis, Elena |
| author_facet | Koudounas, Alkis Savelli, Claudio Giobergia, Flavio Baralis, Elena |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15700 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | "Alexa, can you forget me?" Machine Unlearning Benchmark in Spoken Language Understanding Koudounas, Alkis Savelli, Claudio Giobergia, Flavio Baralis, Elena Computation and Language Sound Audio and Speech Processing 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. |
| title | "Alexa, can you forget me?" Machine Unlearning Benchmark in Spoken Language Understanding |
| topic | Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.15700 |