title: Semantic Entropy Probes: Robust and Cheap Hallucination Detection in
    LLMs
summary: We propose semantic entropy probes (SEPs), a cheap and reliable method for
  uncertainty quantification in Large Language Models (LLMs). Hallucinations,
  which are plausible-sounding but factually incorrect and arbitrary model
  generations, present a major challenge to the practical adoption of LLMs.
  Recent work by Farquhar et al. (2024) proposes semantic entropy (SE), which can
  detect hallucinations by estimating uncertainty in the space semantic meaning
  for a set of model generations. However, the 5-to-10-fold increase in
  computation cost associated with SE computation hinders practical adoption. To
  address this, we propose SEPs, which directly approximate SE from the hidden
  states of a single generation. SEPs are simple to train and do not require
  sampling multiple model generations at test time, reducing the overhead of
  semantic uncertainty quantification to almost zero. We show that SEPs retain
  high performance for hallucination detection and generalize better to
  out-of-distribution data than previous probing methods that directly predict
  model accuracy. Our results across models and tasks suggest that model hidden
  states capture SE, and our ablation studies give further insights into the
  token positions and model layers for which this is the case.
paper_authors._id: 667a6283becec8fc51091263
paper_authors.name: Jannik Kossen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-25T11:18:56.617Z
paper_authors._id: 64df30f2e672e4d2c57049e8
paper_authors.avatarUrl: /avatars/8c9503e43f403d655b11ea5c6e23ae79.svg
paper_authors.fullname: Jannik
paper_authors.type: user
paper_authors.user: jlko
paper_authors._id: 667a6283becec8fc51091264
paper_authors.name: Jiatong Han
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-25T08:45:12.741Z
paper_authors._id: 652531b72eff981222c547c6
paper_authors.avatarUrl: /avatars/e90d729b9ca8e583175e8f61972cf888.svg
paper_authors.fullname: Jiatong Han
paper_authors.type: user
paper_authors.user: Juliushanhanhan
paper_authors._id: 667a6283becec8fc51091265
paper_authors.name: Muhammed Razzak
paper_authors._id: 667a6283becec8fc51091266
paper_authors.name: Lisa Schut
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-25T08:45:22.511Z
paper_authors._id: 65dcca2f7fb5b9f96b1aacbd
paper_authors.avatarUrl: /avatars/df939fc527b4beffcf7d0001710578e1.svg
paper_authors.fullname: Lisa Schut
paper_authors.type: user
paper_authors.user: schutje
paper_authors._id: 667a6283becec8fc51091267
paper_authors.name: Shreshth Malik
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-25T08:45:28.044Z
paper_authors._id: 637f955ce0d5fd31995aeff3
paper_authors.avatarUrl: /avatars/5efafc534010f3025fe3ab862a681369.svg
paper_authors.fullname: Shreshth Malik
paper_authors.type: user
paper_authors.user: s-a-malik
paper_authors._id: 667a6283becec8fc51091268
paper_authors.name: Yarin Gal
paper_title: Semantic Entropy Probes: Robust and Cheap Hallucination Detection in
    LLMs
paper_summary: We propose semantic entropy probes (SEPs), a cheap and reliable method for
  uncertainty quantification in Large Language Models (LLMs). Hallucinations,
  which are plausible-sounding but factually incorrect and arbitrary model
  generations, present a major challenge to the practical adoption of LLMs.
  Recent work by Farquhar et al. (2024) proposes semantic entropy (SE), which can
  detect hallucinations by estimating uncertainty in the space semantic meaning
  for a set of model generations. However, the 5-to-10-fold increase in
  computation cost associated with SE computation hinders practical adoption. To
  address this, we propose SEPs, which directly approximate SE from the hidden
  states of a single generation. SEPs are simple to train and do not require
  sampling multiple model generations at test time, reducing the overhead of
  semantic uncertainty quantification to almost zero. We show that SEPs retain
  high performance for hallucination detection and generalize better to
  out-of-distribution data than previous probing methods that directly predict
  model accuracy. Our results across models and tasks suggest that model hidden
  states capture SE, and our ablation studies give further insights into the
  token positions and model layers for which this is the case.
paper_ai_keywords: semantic entropy probes (SEPs) Large Language Models (LLMs) hallucinations semantic entropy (SE) uncertainty semantic meaning hidden states token positions model layers