DAVinCI combines claim attribution to model internals and external sources with entailment-based verification to improve LLM factual reliability by 5-20% on fact-checking datasets.
Retrieval-Augmented Generation (RAG) frame- works (Lewis et al., 2021) have emerged as a pop- ular solution for grounding LLM outputs in external sources
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Trust but Verify: Introducing DAVinCI -- A Framework for Dual Attribution and Verification in Claim Inference for Language Models
DAVinCI combines claim attribution to model internals and external sources with entailment-based verification to improve LLM factual reliability by 5-20% on fact-checking datasets.