How to Steer LLM Latents for Hallucination Detection?
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.
67ca18ce1994a242bb33524a
Seongheon Park
claimed_verified
2025-03-07T09:09:54.881Z
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Seongheon Park
user
sam121796
67ca18ce1994a242bb33524b
Xuefeng Du
67ca18ce1994a242bb33524c
Min-Hsuan Yeh
claimed_verified
2025-05-22T07:19:02.727Z
63c07f198d1175e3399d2161
https://cdn-avatars.huggingface.co/v1/production/uploads/1673559768829-noauth.jpeg
Min-Hsuan Yeh
user
samuelyeh
67ca18ce1994a242bb33524d
Haobo Wang
67ca18ce1994a242bb33524e
Yixuan Li
How to Steer LLM Latents for Hallucination Detection?
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.
The Truthfulness Separator Vector (TSV) reshapes the latent space of LLMs to enhance the separation between truthful and hallucinated outputs using a two-stage framework with optimal transport and confidence-based filtering.
latent space LLMs hallucination detection Truthfulness Separator Vector steering vector representation space factual accuracy linguistic coherence compact clusters optimal transport-based algorithm pseudo-labeling confidence-based filtering state-of-the-art performance generalization