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Reinforcement learning boosts zero-shot Text-to-SPARQL generation

By PulseAugur Editorial · [1 sources] ·

Researchers have explored the use of reinforcement learning for zero-shot Text-to-SPARQL generation, a task crucial for knowledge graph question answering. They applied Group-Relative Policy Optimization (GRPO) to the Qwen3-1.7B model, utilizing execution feedback and answer-level rewards to train the model without requiring gold query annotations. The study found that outcome-based rewards significantly improved performance over a zero-shot baseline, suggesting reinforcement learning is a viable strategy when full supervision is unavailable. AI

IMPACT Demonstrates a viable reinforcement learning approach for knowledge graph question answering without full supervision.

RANK_REASON Academic paper detailing a novel approach to text-to-SPARQL generation using reinforcement learning. [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 →

Reinforcement learning boosts zero-shot Text-to-SPARQL generation

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27 / 100
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Academic paper detailing a novel approach to text-to-SPARQL generation using reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Jann Pfeifer, Debayan Banerjee, Ricardo Usbeck ·

    Text-to-SPARQL Generation with Reinforcement Learning: A GRPO-based Approach on DBLP

    arXiv:2605.20066v2 Announce Type: replace Abstract: 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 ann…

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