TABQAWORLD improves multi-turn table QA by dynamically selecting multimodal representations and optimizing reasoning trajectories with metadata, delivering 4.87% accuracy gains over baselines and 33.35% latency reduction.
An Inner Table Retriever for Robust Table Question Answering
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
SchemaRAG dynamically reduces large schemas via RAG for LLM information extraction, reporting up to 8.8% micro-F1 gain, 47% latency cut, and 48% token cost reduction on healthcare and e-commerce data.
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TABQAWORLD: Optimizing Multimodal Reasoning for Multi-Turn Table Question Answering
TABQAWORLD improves multi-turn table QA by dynamically selecting multimodal representations and optimizing reasoning trajectories with metadata, delivering 4.87% accuracy gains over baselines and 33.35% latency reduction.
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SchemaRAG: Dynamic Large Schema Reduction for LLM-driven Structured Information Extraction
SchemaRAG dynamically reduces large schemas via RAG for LLM information extraction, reporting up to 8.8% micro-F1 gain, 47% latency cut, and 48% token cost reduction on healthcare and e-commerce data.