Selective Capability Unlearning in End-to-End Spoken Language Understanding
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Computer Science > Computation and Language
Title:Selective Capability Unlearning in End-to-End Spoken Language Understanding
Abstract:Modern spoken language understanding (SLU) systems are increasingly deployed in real-world settings, where specific functionalities may need to be removed due to policy or safety constraints. In SLU, a functionality corresponds to an intent and its associated slot-generation behavior. However, in autoregressive models, suppressing a target intent does not eliminate the conditional mapping that generates slots conditioned on that intent. When the intent prefix is externally supplied, the model can reconstruct the original intent-slot structure. We identify this structural failure as \textbf{\emph{capability persistence}}. We propose \textit{\underline{B}inding \underline{S}ubspace (BSU)}, a representation-level framework that isolates and attenuates intent-conditioned directions underlying this mapping. Across SLU benchmarks, BSU substantially reduces forced-prefix recoverability while preserving retained performance.
| Comments: | 5 pages, 3 figures, preprint |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.24063 [cs.CL] |
| (or arXiv:2606.24063v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24063
arXiv-issued DOI via DataCite (pending registration)
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