Text Gen-
eration and
QA

HVI

HILT (Own
dataset)

T5, XLNet,
T0, BLOOM,
Alpaca, GPT-
4, OPT, Dolly,
GPT-3.5,
LLaMA, MPT,
Vicuna, GPT-
2, StableLM,
GPT-3

Open-
domain QA,
Abstractive
QA, Jeopardy
QG, Fact
Verification

QA

BLEU-1,
Q-BLEU-1,
Rouge-L

T5 11B,
BART

DPR-BERT,
GPT-3

Accuracy,
Expected
Calibration Error
(ECE) and Brier
score

NQ, TriviaQA,
WebQuestions,
CuratedTrec,
MSMARCO,
SearchQA,
FEVER-3,
FEVER-2

NQ, TriviaQA,
and HotpotQA

Open-
domain text
generation,

Self contra
reduced, Informa-
tive facts retained,
Perplexity
increased

GPT-4, GPT-
3.5, Llama2
70B Chat, and
Vicuna

Manual

Short-form
QA, biography
generation,
and reasoning,

Exact match
(EM) and
F1-score,
recall@5,
FACTSCORE

InstructGPT,
Llama 2 7B
Chat,
Llama 2 13B
Chat,
Llama 1 65B
and GPT-3.5

HotPotQA,
TriviaQA,
ALCE-
Qampari
QA
,bio bench-
mark

Continued on the next page

Category

Mitigation
Technique(s)

Title

Table 1 – Continued from the previous page

Detection

Task(s)

Metric(s)

Dataset(s)
AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels