GRACE: Gradient-guided Controllable Retrieval for Augmenting Attribute-based Text Generation (2023.findings-acl)
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| Challenge: | Existing methods for controlling the generation of pre-trained language models infuse domain bias into the generation process, making it difficult to generate out-of-domain texts. |
| Approach: | They propose a retrieval-augmented generation framework that uses retrieval to generate fluent sentences with high attribute relevance. |
| Outcome: | The proposed method can generate fluent sentences with high attribute relevance while keeping domain bias out of the model. |
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| Challenge: | Existing methods rely on separate retrievers to fetch top-k text chunks for generating evidence, and they lack joint optimization. |
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Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies (2020.emnlp-main)
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Facts2Story: Controlling Text Generation by Key Facts (2020.coling-main)
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