Trusting Your Evidence: Hallucinate Less with Context-aware Decoding
Weijia Shi Xiaochuang Han Mike Lewis Yulia Tsvetkov Luke Zettlemoyer Scott Wen-tau Yih
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.