P-Masking: Power Law Masking Improves Multi-attribute Controlled Generation

• URL: http://arxiv.org/abs/2410.24201v1
• Date: Thu, 31 Oct 2024 17:55:45 GMT
• Title: P-Masking: Power Law Masking Improves Multi-attribute Controlled Generation
• Authors: Mohamed Elgaar, Hadi Amiri,
• Abstract summary: LingGen is a novel approach for controlled text generation that offers precise control over a wide array of linguistic attributes. LingGen employs a dynamic P-MASKING strategy, which samples masking rates from a power law distribution during training. Experiments demonstrate that LingGen surpasses current state-of-the-art models in both attribute control accuracy and text fluency.
• Score: 14.763505073094779
• License:
• Abstract: We introduce LingGen, a novel approach for controlled text generation that offers precise control over a wide array of linguistic attributes, even as the number of attributes varies. LingGen employs a dynamic P-MASKING strategy, which samples masking rates from a power law distribution during training. This innovative approach enables the model to develop robust representations and adapt its attribute control capabilities across a variable number of attributes, from a single attribute to multiple complex configurations. The P-MASKING technique enhances LingGen's ability to manage different levels of attribute visibility, resulting in superior performance in multi-attribute generation tasks. Our experiments demonstrate that LingGen surpasses current state-of-the-art models in both attribute control accuracy and text fluency, particularly excelling in scenarios with varying attribute demands. Additionally, our ablation studies highlight the effectiveness of P-MASKING and the influence of different base language models on performance. These findings demonstrate LingGen's potential for applications requiring precise and adaptable control over multiple linguistic attributes in text generation.

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