Calibration Across Layers: Understanding Calibration Evolution in LLMs

TL;DR

This paper investigates how calibration evolves across different layers in large language models, revealing a confidence correction phase and a low-dimensional calibration direction that improve model confidence without affecting accuracy.

Contribution

It uncovers the layer-wise evolution of calibration in LLMs and identifies a calibration direction in the residual stream that enhances calibration metrics.

Findings

Calibration improves in upper layers after decision certainty.

A low-dimensional calibration direction significantly boosts calibration metrics.

Calibration is a distributed phenomenon throughout the network.

Abstract

Large Language Models (LLMs) have demonstrated inherent calibration capabilities, where predicted probabilities align well with correctness, despite prior findings that deep neural networks are often overconfident. Recent studies have linked this behavior to specific components in the final layer, such as entropy neurons and the unembedding matrix null space. In this work, we provide a complementary perspective by investigating how calibration evolves throughout the network depth. Analyzing multiple open-weight models on the MMLU benchmark, we uncover a distinct confidence correction phase in the upper/later layers, where model confidence is actively recalibrated after decision certainty has been reached. Furthermore, we identify a low-dimensional calibration direction in the residual stream whose perturbation significantly improves calibration metrics (ECE and MCE) without harming accuracy. Our findings suggest that calibration is a distributed phenomenon, shaped throughout the network forward pass, not just in its final projection, providing new insights into how confidence-regulating mechanisms operate within LLMs.