Between the Layers Lies the Truth: Uncertainty Estimation in LLMs Using Intra-Layer Local Information Scores

TL;DR

This paper introduces a lightweight, intra-layer agreement-based uncertainty estimation method for LLMs that outperforms probing, especially in transfer and quantized settings, by analyzing cross-layer internal representations.

Contribution

It proposes a novel, compact intra-layer agreement scoring method for uncertainty estimation that is transferable, robust, and efficient across different models and quantization levels.

Findings

Matches probing in-distribution with minimal performance difference.

Outperforms probing in cross-dataset transfer scenarios.

Remains effective under 4-bit weight quantization.

Abstract

Large language models (LLMs) are often confidently wrong, making reliable uncertainty estimation (UE) essential. Output-based heuristics are cheap but brittle, while probing internal representations is effective yet high-dimensional and hard to transfer. We propose a compact, per-instance UE method that scores cross-layer agreement patterns in internal representations using a single forward pass. Across three models, our method matches probing in-distribution, with mean diagonal differences of at most $-1.8$ AUPRC percentage points and $+4.9$ Brier score points. Under cross-dataset transfer, it consistently outperforms probing, achieving off-diagonal gains up to $+2.86$ AUPRC and $+21.02$ Brier points. Under 4-bit weight-only quantization, it remains robust, improving over probing by $+1.94$ AUPRC points and $+5.33$ Brier points on average. Beyond performance, examining specific layer--layer interactions reveals differences in how disparate models encode uncertainty. Altogether, our UE method offers a lightweight, compact means to capture transferable uncertainty in LLMs.