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.- Anthology ID:
- 2022.emnlp-main.223
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3392–3406
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2022.emnlp-main.223/
- DOI:
- 10.18653/v1/2022.emnlp-main.223
- Cite (ACL):
- Hanqing Zhang and Dawei Song. 2022. DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3392–3406, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generation (Zhang & Song, EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2022.emnlp-main.223.pdf