  Grounded generation aims to equip language models (LMs) with the ability to
produce more credible and accountable responses by accurately citing verifiable
sources. However, existing methods, by either feeding LMs with raw or
preprocessed materials, remain prone to errors. To address this, we introduce
CaLM, a novel verification framework. CaLM leverages the insight that a robust
grounded response should be consistent with information derived solely from its
cited sources. Our framework empowers smaller LMs, which rely less on
parametric memory and excel at processing relevant information given a query,
to validate the output of larger LMs. Larger LM responses that closely align
with the smaller LMs' output, which relies exclusively on cited documents, are
verified. Responses showing discrepancies are iteratively refined through a
feedback loop. Experiments on three open-domain question-answering datasets
demonstrate significant performance gains of 1.5% to 7% absolute average
without any required model fine-tuning.
