SAFAARI framework enhances chatbots' data access with schema linking

A new framework called SAFAARI has been developed to improve agentic chatbots' ability to access enterprise data by addressing schema linking challenges in Natural Language to SQL systems. This framework utilizes specialized agents for content, metadata, and orchestration, and introduces a novel metric, SEAL, to evaluate performance. Experiments show SAFAARI achieves an 81.66% SEAL score, a 6.65% improvement over baselines, and reduces development time by 8x while maintaining high accuracy. AI

IMPACT Streamlines API development and enhances self-service capabilities for enterprises with complex data ecosystems.

RANK_REASON The cluster contains a research paper detailing a new framework and metric for Natural Language to SQL systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• Bhanu Teja Rangaraju
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• Natural Language to SQL
• ScienceCast
• SEAL

• paper
• product
• infra

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