title: How to Steer LLM Latents for Hallucination Detection?
summary: 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.
paper_authors._id: 67ca18ce1994a242bb33524a
paper_authors.name: Seongheon Park
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-07T09:09:54.881Z
paper_authors._id: 6696b81167c22a79a15ebaef
paper_authors.avatarUrl: /avatars/57ce0329c4a2c46481818bc99c1d7f17.svg
paper_authors.fullname: Seongheon Park
paper_authors.type: user
paper_authors.user: sam121796
paper_authors._id: 67ca18ce1994a242bb33524b
paper_authors.name: Xuefeng Du
paper_authors._id: 67ca18ce1994a242bb33524c
paper_authors.name: Min-Hsuan Yeh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-22T07:19:02.727Z
paper_authors._id: 63c07f198d1175e3399d2161
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1673559768829-noauth.jpeg
paper_authors.fullname: Min-Hsuan Yeh
paper_authors.type: user
paper_authors.user: samuelyeh
paper_authors._id: 67ca18ce1994a242bb33524d
paper_authors.name: Haobo Wang
paper_authors._id: 67ca18ce1994a242bb33524e
paper_authors.name: Yixuan Li
paper_title: How to Steer LLM Latents for Hallucination Detection?
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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