arXiv:2606.24063v1 Announce Type: cross 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.
As AI models are increasingly deployed in sensitive real-world applications, the need for precise control over their capabilities, including selective unlearning, becomes paramount for compliance and safety.
This research addresses a critical limitation in current autoregressive AI models regarding the complete removal of specific functionalities, which has significant implications for AI governance, safety, and commercial deployment.
The ability to truly 'unlearn' capabilities in AI, rather than just suppress them, changes the paradigm for how models can be updated, regulated, and used in environments requiring dynamic policy adherence.
- · AI safety researchers
- · AI developers
- · Regulated industries
- · AI governance platforms
- · AI models lacking sophisticated unlearning mechanisms
- · Organizations with poor AI risk management
More robust and auditable AI systems can be deployed in highly sensitive domains.
Increased trust in AI systems due to better control over their behaviors and data biases.
New legal and ethical frameworks for 'right to be forgotten' and 'responsible AI' could emerge, directly leveraging advanced unlearning capabilities.
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Read at arXiv cs.AI