AI RESEARCH
Text-to-SPARQL Generation with Reinforcement Learning: A GRPO-based Approach on DBLP
arXiv CS.CL
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ArXi:2605.20066v1 Announce Type: new Knowledge graph question answering seeks to translate natural language questions into executable queries over knowledge graphs, but existing approaches often rely on large models or full supervision in the form of gold query annotations. This study examines whether reinforcement learning with outcome-based rewards can train a small instruction-tuned language model to perform zero-shot Text-to-SPARQL generation in the scholarly domain.