Academic

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

arXiv:2603.22299v1 Announce Type: new 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

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Zvi N. Badash, Yonatan Belinkov, Moti Freiman · · 1 min read · 3 views
#cs.LG #cs.AI

arXiv:2603.22299v1 Announce Type: new 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.

Executive Summary

This article presents a novel approach to uncertainty estimation in large language models (LLMs) using intra-layer local information scores. The proposed method scores cross-layer agreement patterns in internal representations using a single forward pass, achieving competitive results with probing in-distribution and outperforming probing under cross-dataset transfer. The study provides insights into how disparate models encode uncertainty, highlighting the potential of this method for capturing transferable uncertainty in LLMs. The findings suggest that this approach can be robust under weight quantization, making it a promising solution for reliable uncertainty estimation in LLMs.

Key Points

  • ▸ The proposed method scores cross-layer agreement patterns using a single forward pass.
  • ▸ It matches probing in-distribution and outperforms probing under cross-dataset transfer.
  • ▸ The method is robust under 4-bit weight-only quantization.

Merits

Transferable Uncertainty Estimation

The proposed method captures transferable uncertainty in LLMs, enabling reliable uncertainty estimation across different models and datasets.

Lightweight Implementation

The method requires only a single forward pass, making it a computationally efficient solution for uncertainty estimation in LLMs.

Demerits

Limited Evaluation Metrics

The study primarily evaluates the method using AUPRC and Brier score metrics, which may not fully capture the complexity of uncertainty estimation in LLMs.

Lack of Exploratory Analysis

The study does not provide a comprehensive exploratory analysis of the internal representations and layer interactions, which may limit our understanding of the method's behavior.

Expert Commentary

The proposed method represents a significant step forward in uncertainty estimation for LLMs. By leveraging intra-layer local information scores, the method provides a computationally efficient and robust solution for capturing transferable uncertainty in LLMs. However, the study's limitations, such as the limited evaluation metrics and lack of exploratory analysis, highlight the need for further research in this area. As the field of uncertainty estimation continues to evolve, it will be essential to develop more comprehensive evaluation metrics and conduct in-depth exploratory analyses to fully understand the behavior of these methods.

Recommendations

  • ✓ Future studies should focus on developing more comprehensive evaluation metrics for uncertainty estimation in LLMs.
  • ✓ Researchers should conduct in-depth exploratory analyses to better understand the behavior of uncertainty estimation methods in LLMs.

Sources

Original: arXiv - cs.LG

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