Papers with topic-controlled generation

1 papers
Attribute Alignment: Controlling Text Generation from Pre-trained Language Models (2021.findings-emnlp)

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Challenge: Large language models can generate text with sentiment polarity or specific topics without changing the original model parameters.
Approach: They propose a method for controlling text generation by aligning disentangled attribute representations.
Outcome: The proposed method shows large performance gains while maintaining diversity and fluency.

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