aiqbench analysis
This research addresses the critical need for interpretable uncertainty quantification in large language models by introducing "mechanistic uncertainty" based on internal mechanism stability. The authors propose a causal robustness margin metric that correlates with hallucination susceptibility, offering a mechanistically grounded alternative to conventional output-based methods. This work advances the field of AI Safety and reliability by providing tools to better assess model confidence through direct intervention analysis.
Measuring Mechanistic Uncertainty in Large Language Models via Causal Robustness
arXiv:2610.22636v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in critical domains, where their susceptibility to hallucinations makes reliable and interpretable uncertainty quantification essential. We introduce the concept of mechanistic uncertainty, which characterizes uncertainty through the stability of the internal mechanisms supporting a model's response. We propose the causal robustness margin, defined as the minimum internal intervention required to deviate the model's output, and operationalize it using Jacobian Lens (Jlens) concept steering vectors. Across three open-source models and five question-answering datasets, we show incorrect and uncertain responses exhibit smaller margins, indicating greater sensitivity to internal interventions, and the causal robustness margin complements conventional output-based uncertainty methods. Finally, in a controlled knowledge injection experiment, correctly learned facts show increased margins after training, suggesting that causal robustness provides a mechanistically grounded signal of uncertainty.
Abstract:Large language models (LLMs) are increasingly used in critical domains, where their susceptibility to hallucinations makes reliable and interpretable uncertainty quantification essential. We introduce the concept of mechanistic uncertainty, which characterizes uncertainty through the stability of the internal mechanisms supporting a model's response. We propose the causal robustness margin, defined as the minimum internal intervention required to deviate the model's output, and operationalize it using Jacobian Lens (Jlens) concept steering vectors. Across three open-source models and five question-answering datasets, we show incorrect and uncertain responses exhibit smaller margins, indicating greater sensitivity to internal interventions, and the causal robustness margin complements conventional output-based uncertainty methods. Finally, in a controlled knowledge injection experiment, correctly learned facts show increased margins after training, suggesting that causal robustness provides a mechanistically grounded signal of uncertainty.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2610.22636 [cs.CL]
(or arXiv:2610.22636v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.22636 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 228 words
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