Computer Science > Computation and Language
arXiv:2306.00369 (cs)
[Submitted on 1 Jun 2023 (v1), last revised 10 Jun 2023 (this version, v2)]
Title:Focused Prefix Tuning for Controllable Text Generation
View a PDF of the paper titled Focused Prefix Tuning for Controllable Text Generation, by Congda Ma and 4 other authors
View PDFAbstract:In a controllable text generation dataset, there exist unannotated attributes that could provide irrelevant learning signals to models that use it for training and thus degrade their performance. We propose focused prefix tuning(FPT) to mitigate the problem and to enable the control to focus on the desired attribute. Experimental results show that FPT can achieve better control accuracy and text fluency than baseline models in single-attribute control tasks. In multi-attribute control tasks, FPT achieves comparable control accuracy with the state-of-the-art approach while keeping the flexibility to control new attributes without retraining existing models.
| Comments: | Accepted to the ACL 2023 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2306.00369 [cs.CL] |
| (orarXiv:2306.00369v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2306.00369 arXiv-issued DOI via DataCite |
Submission history
From: Congda Ma [view email][v1] Thu, 1 Jun 2023 06:00:43 UTC (7,824 KB)
[v2] Sat, 10 Jun 2023 12:36:48 UTC (7,824 KB)
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View a PDF of the paper titled Focused Prefix Tuning for Controllable Text Generation, by Congda Ma and 4 other authors
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