Abstract
We explore the idea of compressing the prompts used to condition language models, and show that compressed prompts can retain a substantive amount of information about the original prompt. For severely compressed prompts, while fine-grained information is lost, abstract information and general sentiments can be retained with surprisingly few parameters, which can be useful in the context of decode-time algorithms for controllability and toxicity reduction. We find that some complex prompts can be effectively compressed into a single token to guide generation. We also show that compressed prompts are largely compositional, and can be constructed such that they can be used to control independent aspects of generated text.- Anthology ID:
- 2022.findings-emnlp.412
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5621–5634
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.412
- DOI:
- Cite (ACL):
- David Wingate, Mohammad Shoeybi, and Taylor Sorensen. 2022. Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5621–5634, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models (Wingate et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.findings-emnlp.412.pdf