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.
Early work in automated fact-checking focused on datasets such as FEVER (Thorne et al., 2018), whichintroducedthetaskofverifyingclaimsagainst evidence retrieved from Wikipedia
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.