Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text Generation (2023.emnlp-main)
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| Challenge: | Language models often generate fluent and convincing content but can lack consistency with the provided source, resulting in potential inaccuracies. |
| Approach: | They propose a new decoding method that augments the contrastive search framework with context-aware regularization terms to promote tokens that are semantically similar to the provided source while penalizing repetitiveness in the generated text. |
| Outcome: | The proposed method improves faithfulness across various language models while maintaining output diversity comparable to well-performing decoding algorithms. |
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