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Creators/Authors contains: "Yih, Wen-tau"

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  1. ; ; ; ; ; (, NAACL)
    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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    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ; (, ACl Findings)
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  3. ; ; ; ; ; ; ; (, ACL Findings)
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  4. ; ; ; ; ; ; ; ; (, Association for Computational Linguistics)
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  5. ; ; ; ; (, 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP'20, long))
    null (Ed.)
    Accepted Manuscript Full Text Available
  6. ; ; ; (, 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP'19))
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