Air-Decoding: Attribute Distribution Reconstruction for Decoding-Time Controllable Text Generation (2023.emnlp-main)
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| Challenge: | Controllable text generation (CTG) aims to generate text with desired attributes, but current methods lack high levels of controllability. |
| Approach: | They propose a lightweight decoding framework that reconstructs attribute distributions to balance the weights between attribute words and non-attribute words to generate more fluent text. |
| Outcome: | The proposed framework achieves state-of-the-art control performance on multiple CTG tasks. |
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| Challenge: | Existing methods for multi-aspect control suffer from attribute degeneration due to mutual interference of these controllers. |
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| Challenge: | Controlled Text Generation (CTG) aims to produce texts that exhibit specific desired attributes. |
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TARA: Token-level Attribute Relation Adaptation for Multi-Attribute Controllable Text Generation (2024.findings-emnlp)
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| Challenge: | Existing work on multi-attribute controllable text generation ignores interrelations of attributes . recent work defines attribute relations as promotive, but not fixed . |
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| Challenge: | Existing methods for controlling coarse attributes are less effective for finer-grained attributes and suffer from inefficiencies when many attributes must be handled jointly. |
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| Challenge: | Existing studies on controlled text generation focus on single-attribute control, but in practical applications, they lack controllability. |
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| Challenge: | Large language models can generate text with sentiment polarity or specific topics without changing the original model parameters. |
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Tailor: A Soft-Prompt-Based Approach to Attribute-Based Controlled Text Generation (2023.acl-long)
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| Challenge: | Existing work focuses on generating sentences satisfying pre-specified attributes such as topic and sentiment, yet suffers from increases in storage and inference time. |
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Controlled Text Generation with Hidden Representation Transformations (2023.findings-acl)
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