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New GGC Framework Boosts Text-to-SPARQL Accuracy with Selective Correction

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Generator-Gate-Corrector (GGC), a novel framework designed to improve the reliability of text-to-SPARQL generation using large language models. GGC first generates a query, then a gate determines if correction is necessary, and finally, a corrector module refines only the high-risk queries. This selective approach enhances accuracy and efficiency, improving query-level accuracy from 90.23% to 98.33% while reducing inference overhead by 45% compared to correcting all queries. AI

IMPACT Enhances the reliability and efficiency of LLM-based query generation, potentially improving data retrieval accuracy in knowledge graph applications.

RANK_REASON The cluster contains an academic paper detailing a new method for text-to-SPARQL generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New GGC Framework Boosts Text-to-SPARQL Accuracy with Selective Correction

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The cluster contains an academic paper detailing a new method for text-to-SPARQL generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ziyi Yang, Thanh-Son Nguyen, Tuan Anh Nguyen, Lihui Chen ·

    GGC: Selective Query Correction for Reliable Text-to-SPARQL Generation

    arXiv:2607.28082v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities in structured query generation, making them a natural choice for Text-to-SPARQL, which translates natural language questions into executable SPARQL queries over know…

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