How to Steer LLM Latents for Hallucination Detection?
Authors: name: Seongheon Park, name: Xuefeng Du, name: Min-Hsuan Yeh, name: Haobo Wang, name: Yixuan Li

Hallucinations in LLMs pose a significant concern to their safe deployment in
real-world applications. Recent approaches have leveraged the latent space of
LLMs for hallucination detection, but their embeddings, optimized for
linguistic coherence rather than factual accuracy, often fail to clearly
separate truthful and hallucinated content. To this end, we propose the
Truthfulness Separator Vector (TSV), a lightweight and flexible steering vector
that reshapes the LLM's representation space during inference to enhance the
separation between truthful and hallucinated outputs, without altering model
parameters. Our two-stage framework first trains TSV on a small set of labeled
exemplars to form compact and well-separated clusters. It then augments the
exemplar set with unlabeled LLM generations, employing an optimal
transport-based algorithm for pseudo-labeling combined with a confidence-based
filtering process. Extensive experiments demonstrate that TSV achieves
state-of-the-art performance with minimal labeled data, exhibiting strong
generalization across datasets and providing a practical solution for
real-world LLM applications.