The methodology trains SLMs to possess self-
evaluation capabilities, recognizing and correcting
hallucinations and unreliable reasoning, enhancing
predictive accuracy and reliability on various NLP
tasks. Comprehensive experiments demonstrate the
superiority of this method across reasoning tasks,
offering a promising approach to responsibly down-
size LLMs.
DRESS: (Chen et al., 2023) propose using natural
language feedback (NLF), specifically critique and
refinement NLF, to improve alignment with human
preferences and interaction capabilities of large vi-
sion language models (LVLMs). They generalize
conditional reinforcement learning to effectively
incorporate non-differentiable NLF by training the
model to generate corresponding responses con-
ditioned on the NLF. Experiments show relative
improvements in DRESS over prior state-of-the-
art LVLMs in metrics of helpfulness, honesty, and
harmlessness alignment.
MixAlign: Despite having accurate reference
AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels