Trusting Your Evidence: Hallucinate Less with Context-aware Decoding (2024.naacl-short)
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| Challenge: | Language models (LMs) often struggle to pay enough attention to the input context, and generate texts that are unfaithful or contain hallucinations. |
| Approach: | They propose a context-aware decoding technique that amplifies the difference between the output probabilities when a model is used with and without context. |
| Outcome: | The proposed model significantly improves faithfulness of different LM families including OPT, GPT, LLaMA, and FLAN-T5 for summarization tasks. |
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