DUD: Decoupled Update Dynamics for Reliable Uncertainty Quantification in Large Language Models
Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs), yet traditional probability-based metrics often fail to capture the model's true epistemic state. While recent mechanistic approaches leverage hidden state dynamics, they typically aggregate residual stream updates, conflating the distinct roles of parametric memory (Feed-Forward Networks) and contextual processing (Attention). We argue that this aggregation obscures fine-grained mechanistic conflicts, such as memory-context misalignment, that are fundamental indicators of uncertainty. To address this, we introduce \textbf{D}ecoupled \textbf{U}pdate \textbf{D}ynamics \textbf{(DUD)...
| Authors | Yixin Bu, Runze Xia, Guanyun Zou et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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