@inproceedings{qian2022controllable,
author = {Qian, Jing and Dong, Li and Shen, Yelong and Wei, Furu and Chen, Weizhu},
title = {Controllable Natural Language Generation with Contrastive Prefixes},
booktitle = {ACL 2022},
year = {2022},
month = {May},
abstract = {To guide the generation of large pretrained language models (LM), previous work has focused on directly fine-tuning the language model or utilizing an attribute discriminator. In this work, we propose a novel lightweight framework for controllable GPT2 generation, which utilizes a set of small attribute-specific vectors, called prefixes, to steer natural language generation. Different from prefix-tuning, where each prefix is trained independently, we take the relationship among prefixes into consideration and train multiple prefixes simultaneously. We propose a novel supervised method and also an unsupervised method to train the prefixes for single-aspect control while the combination of these two methods can achieve multi-aspect control. Experimental results on both single-aspect and multi-aspect control show that our methods can guide generation towards the desired attributes while keeping high linguistic quality.},
url = {http://approjects.co.za/?big=en-us/research/publication/controllable-natural-language-generation-with-contrastive-prefixes/},
}