AI RESEARCH
FT-RAG: A Fine-grained Retrieval-Augmented Generation Framework for Complex Table Reasoning
arXiv CS.CL
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ArXi:2605.01495v1 Announce Type: new Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding responses in external knowledge during inference. However, conventiona RAG systems under-perform on structured tabular data, largely due to coarse retrieval granularity and insufficient table semantic comprehension. To address these limitations, we