Trusting Your Evidence: Hallucinate Less with Context-aware Decoding

• URL: http://arxiv.org/abs/2305.14739v1
• Date: Wed, 24 May 2023 05:19:15 GMT
• Title: Trusting Your Evidence: Hallucinate Less with Context-aware Decoding
• Authors: Weijia Shi, Xiaochuang Han, Mike Lewis, Yulia Tsvetkov, Luke Zettlemoyer, Scott Wen-tau Yih
• Abstract summary: Language models (LMs) often struggle to pay enough attention to the input context, and generate texts that are unfaithful or contain hallucinations. We present context-aware decoding (CAD), which follows a contrastive output distribution that amplifies the difference between the probabilities when a model is used with and without context.
• Score: 91.91468712398385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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