"According to ...": Prompting Language Models Improves Quoting from Pre-Training Data

Understand

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.