Reason and Verify: A Framework for Faithful Retrieval-Augmented Generation

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• The proposed RAG framework integrates neural query rewriting, BGE-based cross-encoder reranking, and rationale generation to ground sub-claims in specific evidence spans.method 95%
• The framework introduces an eight-category verification taxonomy for fine-grained assessment of rationale faithfulness, distinguishing between explicit and implicit support patterns.method 95%
• Explicit rationale generation improves accuracy over vanilla RAG baselines on biomedical QA benchmarks.result 90%
• Dynamic demonstration selection combined with robust reranking yields further gains in few-shot settings.result 90%
• Using Llama-3-8B-Instruct, the approach achieves 89.1% on BioASQ-Y/N and 73.0% on PubMedQA, competitive with systems using significantly larger models.result 95%
• Standard RAG pipelines often lack mechanisms to verify intermediate reasoning, leaving them vulnerable to hallucinations in high-stakes domains.limitation 85%
• Explicit rationale generation improves system transparency and enables more detailed diagnosis of retrieval failures in biomedical question answering.result 85%
• The framework was evaluated on BioASQ and PubMedQA benchmarks with analysis of dynamic in-context learning and reranking under constrained token budgets.method 90%

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