S2CR: A self-supervised self-consistency reasoning framework coupled to retrieval-augmented generation

• First to shift self-consistency exploration from post-hoc optimization to the process modeling. • We propose S 2 CR , a self-supervised reasoning framework coupled to RAG. • RAG plays a dual role: enhancing factual grounding and enabling consistency evaluation. • Employs triple-based self-checking to evaluate reasoning consistency. Existing approaches to the self-consistency exploration in Large Language Models (LLMs) primarily rely on post-hoc selection, overlooking the inherent logical structures essential for reasoning. To address this gap, we pioneer a new perspective by introducing S 2 CR , a self-supervised reasoning framework that presents the LLM consistency from internal modeling while coupling retrieval-augmented generation (RAG) for knowledge integration and process supervision. This framework operates in four stages: Information Retrieval patches the parametric knowledge of LLMs and provides factual grounding for evaluation; Response Generation generates multiple candidate responses for input to explore diverse reasoning paths; Consistency Evaluation quantifies logical consistency by aligning extracted triples from both the generated responses and retrieval information; and Duality Synergy Optimization (DSOP) further bolsters the consistency performance through two complementary modules, Introspection-Driven Self-correction Guidance (IDSG) and Fine-Grained Consensus Alignment (FGCA). Experiments conducted on three public datasets POPQA, Biography, and ALCE-ASQA demonstrate that S 2 CR achieves objective quantification of internal self-consistency and significantly improves performance ranging from 3.19% to 23.49% over Baseline ⋄ across diverse foundational LLMs, e.g. , GPT-3.5-turbo, GPT-4o, and open-source models LLaMA3-8B and LLaMA3-70B.