@inproceedings{pei-etal-2023-preadd,
title = "{PREADD}: Prefix-Adaptive Decoding for Controlled Text Generation",
author = "Pei, Jonathan and
Yang, Kevin and
Klein, Dan",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.636/",
doi = "10.18653/v1/2023.findings-acl.636",
pages = "10018--10037",
abstract = "We propose Prefix-Adaptive Decoding (PREADD), a flexible method for controlled text generation. Unlike existing methods that use auxiliary expert models to control for attributes, PREADD does not require an external model, instead relying on linearly combining output logits from multiple prompts. Specifically, PREADD contrasts the output logits generated using a raw prompt against those generated using a prefix-prepended prompt, enabling both positive and negative control with respect to any attribute encapsulated by the prefix. We evaluate PREADD on three tasks{---}toxic output mitigation, gender bias reduction, and sentiment control{---}and find that PREADD outperforms not only prompting baselines, but also an auxiliary-expert control method, by 12{\%} or more in relative gain on our main metrics for each task."
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%0 Conference Proceedings
%T PREADD: Prefix-Adaptive Decoding for Controlled Text Generation
%A Pei, Jonathan
%A Yang, Kevin
%A Klein, Dan
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F pei-etal-2023-preadd
%X We propose Prefix-Adaptive Decoding (PREADD), a flexible method for controlled text generation. Unlike existing methods that use auxiliary expert models to control for attributes, PREADD does not require an external model, instead relying on linearly combining output logits from multiple prompts. Specifically, PREADD contrasts the output logits generated using a raw prompt against those generated using a prefix-prepended prompt, enabling both positive and negative control with respect to any attribute encapsulated by the prefix. We evaluate PREADD on three tasks—toxic output mitigation, gender bias reduction, and sentiment control—and find that PREADD outperforms not only prompting baselines, but also an auxiliary-expert control method, by 12% or more in relative gain on our main metrics for each task.
%R 10.18653/v1/2023.findings-acl.636
%U https://aclanthology.org/2023.findings-acl.636/
%U https://doi.org/10.18653/v1/2023.findings-acl.636
%P 10018-10037
Markdown (Informal)
[PREADD: Prefix-Adaptive Decoding for Controlled Text Generation](https://aclanthology.org/2023.findings-acl.636/) (Pei et al., Findings 2023)
ACL