AINeutralarXiv – CS AI · 14h ago6/10
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SERC: LDPC-Inspired Semantic Error Correction for Retrieval-Augmented Generation
Researchers propose SERC, an LDPC-inspired framework that treats LLM hallucination correction as a semantic error-correction problem using sparse verification strategies. The training-free, model-agnostic approach demonstrates superior performance on factual accuracy benchmarks while reducing computational overhead compared to dense verification methods.
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