  Recognizing if LLM output can be grounded in evidence is central to many
tasks in NLP: retrieval-augmented generation, summarization, document-grounded
dialogue, and more. Current approaches to this kind of "fact-checking" are
based on verifying each piece of a model generation against potential evidence
using an LLM. However, this process can be very computationally expensive,
requiring many calls to LLMs to check a single response. In this work, we show
how to build small models that have GPT-4-level performance but for 400x lower
cost. We do this by constructing synthetic training data with GPT-4, which
involves creating realistic yet challenging instances of factual errors via a
structured generation procedure. Training on this data teaches models to check
each fact in the claim and recognize synthesis of information across sentences.
For evaluation, we unify pre-existing datasets into a benchmark LLM-AggreFact,
collected from recent work on fact-checking and grounding LLM generations. Our
best system MiniCheck-FT5 (770M parameters) outperforms all systems of
comparable size and reaches GPT-4 accuracy. We release LLM-AggreFact, code for
data synthesis, and models.
