Details not
provided

• It displays
limited impact
on tasks that
are directly
formulated
as language
modeling, such
as coreference
resolution and
commonsense
reasoning.
• It requires
designing a
synthetic task,
and reduces
hallucination on
some models
more than
others.

Category

Developing
Models

Mitigation
Technique(s)

Introducing
new decoding

strategy

Table 1 – Continued from the previous page

Title

Detection

Task(s)

Metric(s)

Dataset(s)

Limitation(s)

Summarization,
Knowledge
Conflicts

ROUGE-L,
BERT-Precision,
FactKB

CNN-DM,
XSUM,
MemoTrap,
NQ-Swap

Details not
provided

×

✓

Trusting Your
Evidence:
Hallucinate Less
with Context-aware
Decoding (Shi et al.,
2023)

DOLA: Decoding by
Contrasting Layers
Improves Factuality
in Large Language
Models (Chuang
et al., 2023)

Evaluated
LLM(s)

OPT(13B and
30B), GPT-
Neo (2.7B and
20B), LLaMA
(13B and 30B)
and FLAN-T5
(XL 3B and
XXL 11B)

LLaMA-(7B,
13B, 33B,
65B) and
GPT4

GPT-4 automatic
evaluation
AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels