2 papers
cs.CL2026
FT-RAG: A Fine-grained Retrieval-Augmented Generation Framework for Complex Table Reasoning
Zebin Guo, Weidong Geng, Ruichen Mao
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding responses in external knowledge during inference. However, conventiona RAG systems under-per…
cs.SE2025
GeoJSON Agents:A Multi-Agent LLM Architecture for Geospatial Analysis-Function Calling vs Code Generation
Qianqian Luo, Qingming Lin, Liuchang Xu +6
Large Language Models (LLMs) have demonstrated substantial progress in task automation and natural language understanding. However, without domain expertise in geographic informati…