Researchers introduced R2VC, a modular retrieve, reason, verify, and calibrate architecture for evidence-grounded fact checking, reporting that an 8B backbone with R2VC achieves 13.74% higher accuracy…
Researchers present B1ade, an efficient retrieval-augmented generation (RAG) architecture with a 335M parameter embedding model and a 1B parameter small language model (SLM). B1ade-embed achieves top …
A developer improved a multilingual fact-checker's F1 score from 0.655 to 0.813 by fixing a fundamental input error: the model was trained on claims alone instead of claim-evidence pairs. The XLM-RoBE…
Researchers found that a linear probe applied to mid-layer hidden states of quantized large language models can detect hallucinations with up to 1.000 AUROC, significantly outperforming sampling-based…