DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generation

• URL: http://arxiv.org/abs/2210.09551v1
• Date: Tue, 18 Oct 2022 02:59:06 GMT
• Title: DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generation
• Authors: Hanqing Zhang and Dawei Song
• Abstract summary: We propose a new CTG approach, namely DisCup, which incorporates the attribute knowledge of discriminator to optimize the control-prompts. DisCup can achieve a new state-of-the-art control performance while maintaining an efficient and high-quality text generation, only relying on around 10 virtual tokens.
• Score: 6.844825905212349
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Prompt learning with immensely large Casual Language Models (CLMs) has been shown promising for attribute-controllable text generation (CTG). However, vanilla prompt tuning tends to imitate training corpus characteristics beyond the control attributes, resulting in a poor generalization ability. Moreover, it is less able to capture the relationship between different attributes, further limiting the control performance. In this paper, we propose a new CTG approach, namely DisCup, which incorporates the attribute knowledge of discriminator to optimize the control-prompts, steering a frozen CLM to produce attribute-specific texts. Specifically, the frozen CLM model, capable of producing multitudinous texts, is first used to generate the next-token candidates based on the context, so as to ensure the diversity of tokens to be predicted. Then, we leverage an attribute-discriminator to select desired/undesired tokens from those candidates, providing the inter-attribute knowledge. Finally, we bridge the above two traits by an unlikelihood objective for prompt-tuning. Extensive experimental results show that DisCup can achieve a new state-of-the-art control performance while maintaining an efficient and high-quality text generation, only relying on around 10 virtual tokens.

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