ParaRel,
MMLU,
WiCE,
HotpotQA and
FEVER

Continued on the next page

• It requires
pre-training
each language
model in a two-
stage training
mechanism to
mitigate text
hallucination.
• It uses
machine
translation
to construct
training data,
which may limit
feasibility for
other languages.
Translation
errors may also
limit metric
quality.
• The weighted-
loss approach
has inconsistent
gains in faith-
fulness across
languages.
• The study
includes only
one example of
a weak open-
source LLM
(BLOOM7B)
and concen-
trated solely
on the NBA
domain for
analysis.
• Relies on
automatically
generated
questions.

Details not
provided

• The work
concentrates on
models with a
parameter limit
of up to 3B.
• It addresses
the reduction
of LLM
hallucinations
in information-
seeking
dialogue
without
intervening in
the knowledge
retrieval
component.

Details not
provided

Category

Mitigation
Technique(s)

Title

Table 1 – Continued from the previous page

Detection

Task(s)

Metric(s)

Dataset(s)
AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels