SERC: LDPC-Inspired Semantic Error Correction for Retrieval-Augmented Generation

ArXi:2605.28837v1 Announce Type: cross While Large Language Models (LLMs) have nstrated remarkable capabilities, their reliability is significantly compromised by hallucinations. Existing intrinsic self-correction methods attempt to address this, but often fail due to self-bias, where models struggle to identify errors in their own outputs without external verification. To overcome these limitations, we propose the LDPC-inspired semantic error correction for retrieval-augmented generation (SERC), providing a theoretical framework to interpret and mitigate LLM hallucinations.