  Language models (LMs) often struggle to pay enough attention to the input
context, and generate texts that are unfaithful or contain hallucinations. To
mitigate this issue, we present context-aware decoding (CAD), which follows a
contrastive output distribution that amplifies the difference between the
output probabilities when a model is used with and without context. Our
experiments show that CAD, without additional training, significantly improves
the faithfulness of different LM families, including OPT, GPT, LLaMA and
FLAN-T5 for summarization tasks (e.g., 14.3% gain for LLaMA in factuality
metrics). Furthermore, CAD is particularly effective in overriding a model's
prior knowledge when it contradicts the provided context, leading to
substantial improvements in tasks where resolving the knowledge conflict is
essential.
