“According to . . . ”: Prompting Language Models Improves Quoting from Pre-Training Data
Orion Weller, Marc Marone, Nathaniel Weir, Dawn Lawrie, Daniel Khashabi, Benjamin Van Durme
Abstract
Large Language Models (LLMs) may hallucinate and generate fake information, despite pre-training on factual data. Inspired by the journalistic device of “according to sources”, we propose according-to prompting: directing LLMs to ground responses against previously observed text. To quantify this grounding, we propose a novel evaluation metric (QUIP-Score) that measures the extent to which model-produced answers are directly found in underlying text corpora. We illustrate with experiments on three corpora (Wikipedia, PubMed, and the U.S. legal tax code) that these prompts improve grounding under our metrics, with the additional benefit of often improving end-task performance. Furthermore, prompts that ask the model to decrease grounding (or to ground to other corpora) indeed decrease QUIP-Score, indicating the ability of LLMs to increase or decrease grounded generations on request.- Anthology ID:
- 2024.eacl-long.140
- Volume:
- Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian’s, Malta
- Editors:
- Yvette Graham, Matthew Purver
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2288–2301
- Language:
- URL:
- https://aclanthology.org/2024.eacl-long.140
- DOI:
- Cite (ACL):
- Orion Weller, Marc Marone, Nathaniel Weir, Dawn Lawrie, Daniel Khashabi, and Benjamin Van Durme. 2024. “According to . . . ”: Prompting Language Models Improves Quoting from Pre-Training Data. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2288–2301, St. Julian’s, Malta. Association for Computational Linguistics.
- Cite (Informal):
- “According to . . . ”: Prompting Language Models Improves Quoting from Pre-Training Data (Weller et al., EACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2024.eacl-long.140.pdf