The plug-and-play framework allows seamless
deployment across contexts with competitive hallu-
cination detection and reduction performance while
preserving text quality.

2.1.3 Prompt Tuning
Prompt tuning is a technique that involves adjusting
the instructions provided to a pre-trained LLM dur-
ing the fine-tuning phase to make the model more
effective at specific tasks. The LLM learns from
‘Soft Prompts’, which are not predetermined but are
instead learned by the model through backpropaga-
tion during the fine-tuning (Lester et al., 2021). For
hallucination mitigation, the following techniques,
which involve prompt tuning, have been proposed
as of now:
Universal Prompt Retrieval for Improving zero-
Shot Evaluation (UPRISE): (Cheng et al., 2023)
propose UPRISE, which tunes a lightweight
AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels