aiqbench analysis
This research introduces SCOPE, a framework that enhances Large Language Model safety by identifying out-of-distribution inputs at the hidden layer level rather than relying solely on final outputs. By combining conformal prediction with supermartingale e-processes, the method provides theoretical guarantees for filtering unsupported requests before generation occurs. The study reveals distinct geometric patterns of service boundaries across different model architectures, offering a more robust approach to managing LLM service scopes.
SCOPE: Sequential Conformal Probing for Reliable OOD Rejection in LLM Services
arXiv:2606.21255v2 Announce Type: replace Abstract: Rejecting inputs outside the defined in-distribution (IND) service scope is critical for large language model (LLM) services, where unsupported requests should be filtered before full generation. Existing out-of-distribution (OOD) detectors often rely on final outputs or final-layer representations, leaving unclear where service-boundary signals are most clearly encoded inside the model; they also lack a theoretical guarantee for held-out inputs. In this paper, we introduce SCOPE (Sequential Conformal OOD Probing and Evaluation), a framework that selects a readable hidden layer, constructs a conformal gate with IND calibration, and uses a supermartingale e-process to certify persistent service-boundary evidence. Experiments across multiple LLM backbones and six carefully designed boundary conditions show that SCOPE improves gate-level rejection over standard final-layer detectors, while revealing how different OOD boundaries take different geometric forms in hidden space.
Abstract:Rejecting inputs outside the defined in-distribution (IND) service scope is critical for large language model (LLM) services, where unsupported requests should be filtered before full generation. Existing out-of-distribution (OOD) detectors often rely on final outputs or final-layer representations, leaving unclear where service-boundary signals are most clearly encoded inside the model; they also lack a theoretical guarantee for held-out inputs. In this paper, we introduce SCOPE (Sequential Conformal OOD Probing and Evaluation), a framework that selects a readable hidden layer, constructs a conformal gate with IND calibration, and uses a supermartingale e-process to certify persistent service-boundary evidence. Experiments across multiple LLM backbones and six carefully designed boundary conditions show that SCOPE improves gate-level rejection over standard final-layer detectors, while revealing how different OOD boundaries take different geometric forms in hidden space.
Computation and Language (cs.CL)
arXiv:2606.21255 [cs.CL]
(or arXiv:2606.21255v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.21255 Focus to learn more arXiv-issued DOI via DataCite
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