  Despite their impressive capabilities, large language models (LLMs) are prone
to hallucinations, i.e., generating content that deviates from facts seen
during pretraining. We propose a simple decoding strategy for reducing
hallucinations with pretrained LLMs that does not require conditioning on
retrieved external knowledge nor additional fine-tuning. Our approach obtains
the next-token distribution by contrasting the differences in logits obtained
from projecting the later layers versus earlier layers to the vocabulary space,
exploiting the fact that factual knowledge in an LLMs has generally been shown
to be localized to particular transformer layers. We find that this Decoding by
Contrasting Layers (DoLa) approach is able to better surface factual knowledge
and reduce the generation of incorrect facts. DoLa consistently improves the
truthfulness across multiple choices tasks and open-ended generation tasks, for
example improving the performance of LLaMA family models on TruthfulQA by
12-17% absolute points, demonstrating its potential in making LLMs reliably
generate truthful facts.
