Fine-Grained Controllable Text Generation Using Non-Residual Prompting

Fredrik Carlsson, Joey Öhman, Fangyu Liu, Severine Verlinden, Joakim Nivre, Magnus Sahlgren


Abstract
The introduction of immensely large Causal Language Models (CLMs) has rejuvenated the interest in open-ended text generation. However, controlling the generative process for these Transformer-based models is at large an unsolved problem. Earlier work has explored either plug-and-play decoding strategies, or more powerful but blunt approaches such as prompting. There hence currently exists a trade-off between fine-grained control, and the capability for more expressive high-level instructions. To alleviate this trade-off, we propose an encoder-decoder architecture that enables intermediate text prompts at arbitrary time steps. We propose a resource-efficient method for converting a pre-trained CLM into this architecture, and demonstrate its potential on various experiments, including the novel task of contextualized word inclusion. Our method provides strong results on multiple experimental settings, proving itself to be both expressive and versatile.
Anthology ID:
2022.acl-long.471
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6837–6857
Language:
URL:
https://aclanthology.org/2022.acl-long.471
DOI:
10.18653/v1/2022.acl-long.471
Bibkey:
Cite (ACL):
Fredrik Carlsson, Joey Öhman, Fangyu Liu, Severine Verlinden, Joakim Nivre, and Magnus Sahlgren. 2022. Fine-Grained Controllable Text Generation Using Non-Residual Prompting. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6837–6857, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Fine-Grained Controllable Text Generation Using Non-Residual Prompting (Carlsson et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.471.pdf
Code
 freddefrallan/non-residual-prompting
Data
C4CommonGen