2.1.1.3 After generation
The following techniques employ the information
retrieval system after generating the entirety of its
output:
Retrofit Attribution using Research and Revi-
sion (RARR): (Gao et al., 2023) In the realm of
LLMs, notable advancements have been achieved
across various tasks; however, issues persist, such
as generating content without proper support or ac-
curacy. The challenge of determining trustworthi-
ness in LLM outputs, due to a lack of attributability,
prompted the introduction of RARR. This model-
agnostic system, presented in the introduction, au-
tomates the attribution process for any text gen-
eration model.
Inspired by fact-checking work-
flows, RARR conducts research and post-editing
to align content with retrieved evidence while pre-
serving original qualities, operating seamlessly af-
ter LLM generation. Contributions outlined in the
introduction encompass formalizing the Editing for
Attribution task, introducing new metrics, bench-
AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels