harmlessness alignment.
MixAlign: Despite having accurate reference
points, LLMs may disregard them and rely on in-
correct references or biases instead. This tendency
to hallucinate arises when users ask questions that
do not directly align with the retrieved references,
lacking detailed knowledge of the stored informa-
tion. (Zhang et al., 2023b) focus on this knowl-
edge alignment problem and introduce MixAlign,
a framework that interacts with both the user and
knowledge base to clarify how the user question
relates to the stored information. MixAlign uses a
language model to achieve automatic knowledge
alignment and, if needed, further enhances this
alignment through user clarifications. MixAlign fo-
cuses on utilizing grounding knowledge for faithful
decision-making. In cases of uncertainty or unclear
evidence, MixAlign generates a question seeking
clarification from the user - a process referred to as
human-assisted knowledge alignment.
AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels