  Retrieval-augmented language models (RALMs) have shown strong performance and
wide applicability in knowledge-intensive tasks. However, there are significant
trustworthiness concerns as RALMs are prone to generating unfaithful outputs,
including baseless information or contradictions with the retrieved context.
This paper proposes SynCheck, a lightweight monitor that leverages fine-grained
decoding dynamics including sequence likelihood, uncertainty quantification,
context influence, and semantic alignment to synchronously detect unfaithful
sentences. By integrating efficiently measurable and complementary signals,
SynCheck enables accurate and immediate feedback and intervention, achieving
0.85 AUROC in detecting faithfulness errors across six long-form
retrieval-augmented generation tasks, improving prior best method by 4%.
Leveraging SynCheck, we further introduce FOD, a faithfulness-oriented decoding
algorithm guided by beam search for long-form retrieval-augmented generation.
Empirical results demonstrate that FOD outperforms traditional strategies such
as abstention, reranking, or contrastive decoding significantly in terms of
faithfulness, achieving over 10% improvement across six datasets.
