FACTORY: A Challenging Human-Verified Prompt Set for Long-Form
  Factuality
Authors: name: Mingda Chen, name: Yang Li, name: Xilun Chen, name: Adina Williams, name: Gargi Ghosh, name: Scott Yih

Long-form factuality evaluation assesses the ability of models to generate
accurate, comprehensive responses to short prompts. Existing benchmarks often
lack human verification, leading to potential quality issues. To address this
limitation, we introduce FACTORY, a large-scale, human-verified prompt set.
Developed using a model-in-the-loop approach and refined by humans, FACTORY
includes challenging prompts that are fact-seeking, answerable, and
unambiguous. We conduct human evaluations on 6 state-of-the-art language models
using FACTORY and existing datasets. Our results show that FACTORY is a
challenging benchmark: approximately 40% of the claims made in the responses of
SOTA models are not factual, compared to only 10% for other datasets. Our
analysis identifies the strengths of FACTORY over prior benchmarks, emphasizing
its reliability and the necessity for models to reason across long-tailed
facts.