Semantic Entropy Probes: Robust and Cheap Hallucination Detection in
  LLMs
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.
667a6283becec8fc51091263
Jannik Kossen
claimed_verified
2024-06-25T11:18:56.617Z
64df30f2e672e4d2c57049e8
/avatars/8c9503e43f403d655b11ea5c6e23ae79.svg
Jannik
user
jlko
667a6283becec8fc51091264
Jiatong Han
admin_assigned
2024-06-25T08:45:12.741Z
652531b72eff981222c547c6
/avatars/e90d729b9ca8e583175e8f61972cf888.svg
Jiatong Han
user
Juliushanhanhan
667a6283becec8fc51091265
Muhammed Razzak
667a6283becec8fc51091266
Lisa Schut
admin_assigned
2024-06-25T08:45:22.511Z
65dcca2f7fb5b9f96b1aacbd
/avatars/df939fc527b4beffcf7d0001710578e1.svg
Lisa Schut
user
schutje
667a6283becec8fc51091267
Shreshth Malik
admin_assigned
2024-06-25T08:45:28.044Z
637f955ce0d5fd31995aeff3
/avatars/5efafc534010f3025fe3ab862a681369.svg
Shreshth Malik
user
s-a-malik
667a6283becec8fc51091268
Yarin Gal
Semantic Entropy Probes: Robust and Cheap Hallucination Detection in
  LLMs
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.
SEPs offer an efficient method for detecting hallucinations in LLMs by approximating semantic entropy from a single model generation's hidden states.
semantic entropy probes (SEPs) Large Language Models (LLMs) hallucinations semantic entropy (SE) hidden states probing methods