Abstract
Recently, pretrained language models (PLMs) have had exceptional success in language generation. To leverage the rich knowledge encoded by PLMs, a simple yet powerful paradigm is to use prompts in the form of either discrete tokens or continuous embeddings. In existing studies, these prompting methods are typically independent of the inputs, lacking sufficient consideration of input semantics. To address this issue, we propose a novel continuous prompting approach, called context-tuning, to fine-tuning PLMs for natural language generation. Firstly, the prompts are derived based on the input text to elicit useful knowledge from PLMs for generation. We refer to such prompts as contextualized prompts. Secondly, we use continuous inverse prompting to improve the process of natural language generation by modeling an inverse generation process from output to input, making the generated text more relevant to the inputs. Furthermore, we utilize a lightweight context-tuning method that fine-tunes only 0.12% of the parameters while maintaining good performance. Our code is publicly available at https://github.com/RUCAIBox/Context-Tuning.- Anthology ID:
- 2022.coling-1.552
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 6340–6354
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.552
- DOI:
- Cite (ACL):
- Tianyi Tang, Junyi Li, Wayne Xin Zhao, and Ji-Rong Wen. 2022. Context-Tuning: Learning Contextualized Prompts for Natural Language Generation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6340–6354, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Context-Tuning: Learning Contextualized Prompts for Natural Language Generation (Tang et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2022.coling-1.552.pdf
- Code
- rucaibox/context-tuning