Category

Title

Detection

Task(s)

Metric(s)

Dataset(s)

Limitation(s)

Mitigation
Technique(s)

Introducing
faithfulness-
based loss
function

Table 1 – Continued from the previous page

Video-
grounded
Dialogues

BLEU,
METEOR,
ROUGE-L,
CIDEr

Evaluated
LLM(s)

T5

AVSD@
DSTC7,
AVSD@
DSTC8

Multilingual
Summarisa-
tion

faithfulness,
DAE, QAFactE-
val,ENFS%,
EntFA, ROUGE-
1/2/L scores

BLOOMZ-P3-
7.1B, Vicuna,
Phoenix-7B,

XL-Sum

Supervised
finetuning

✓

QA

HaloCheck (Own
metric)

BLOOM 7B,
GPT-4

Manual

QA,

F1-score

T5

CFTriviaQA

Biography
generation and
Medical QA

FactScore

Llama1 and
Llama2-chat

Manual

Details not
provided

QA

BLEU, ROUGE,
BERTScore and
Precision

Flan-T5 (Base,
Large and XL)

FaithDial,
TopiOCQA
and DoQA

×

QA

R-Tuning: Teaching
Large Language
Models to Refuse
Unknown Questions
(Zhang et al., 2023a)

Accuracy, Av-
erage Precision
(AP)

OpenLLaMA-
3B, LLaMA-
7B and
LLaMA-13B

ParaRel,
MMLU,
WiCE,
HotpotQA and
FEVER

Continued on the next page
AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels