MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents
- URL: http://arxiv.org/abs/2404.10774v1
- Date: Tue, 16 Apr 2024 17:59:10 GMT
- Title: MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents
- Authors: Liyan Tang, Philippe Laban, Greg Durrett,
- Abstract summary: We show how to build small models that have GPT-4-level performance but for 400x lower cost.
We unify pre-existing datasets into a benchmark LLM-AggreFact.
Our best system MiniCheck-FT5 (770M parameters) outperforms all systems of comparable size and reaches GPT-4 accuracy.
- Score: 62.02920842630234
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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