Harnessing the Latent Space: From Steering Vectors to Model Calibrators for Control and Trust

2026-06-30

AI Summary

This work addresses the challenges posed by the complex internal representations of large language models, which hinder controllable behavior and reliable assessment of output trustworthiness. The paper introduces a novel approach that leverages the latent space simultaneously for behavior steering and confidence calibration. Specifically, it constructs steering vectors to enable targeted control over model behavior and designs a latent-space-based calibrator to quantify the reliability of generated outputs. This dual-use framework not only uncovers the latent space’s pivotal role in enhancing both controllability and reliability but also offers a unified foundation for building trustworthy language models. Empirical evaluations across multiple benchmark tasks demonstrate the effectiveness of the proposed method.

Abstract

Language models have changed from unreliable text generators to highly-capable large models with trillions of parameters. Capability increases come hand-in-hand with increases in scale, making understanding the internal representations of models more challenging. Since millions of users increasing rely on language models to interact with external tools or make decisions in medium or high-stakes scenarios, we need to establish control over model behavior and know when to trust model outputs. In this paper, we discuss our contributions on harnessing the latent spaces by proposing steering vectors for control and developing latent space-based model calibrators for trust. Together, our contributions help demystify the latent spaces of language models and offer new insights into how to harness model internals to build more trustworthy language technology.

Problem

• latent space
• language models
• model control
• trustworthiness
• model calibration

Innovation

• latent space
• steering vectors
• model calibrators
• trustworthy AI
• language model control