Reflect-SQL: A Self-Reflection Based Framework for Text-to-SQL

arXiv:2609.02944 · cs.IR, cs.AI · Submitted 2026-09-01 · Read on arXiv

cs.IR, cs.AI

Submitted: 2026-09-01

Updated: 2026-09-01

Comments: Accepted in PAKDD 2026

Journal ref: In: Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2026 Workshops), pp. 74-86, Springer, 2026. Print ISBN: 978-981-92-2013-7, eBook ISBN: 978-981-92-2014-4

DOI: 10.1007/978-981-92-2014-4_7

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Text-to-SQL — 19× in this paper · explained in 3 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• Consistency — 7× in this paper · explained in 3 episodes
• RAG — 7× in this paper · explained in 3 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• In-Context Learning — 5× in this paper · explained in 4 episodes
• LLM-as-a-Judge — 5× in this paper · explained in 3 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Retrieval-Augmented Generation — 4× in this paper
• Ablation — 3× in this paper · explained in 1 episode
• Aggregation — 3× in this paper · explained in 1 episode
• Iterative Refinement — 3× in this paper · explained in 3 episodes
• Knowledge Base Construction — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Semantic Alignment — 2× in this paper · explained in 3 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Multi-agent Collaborative Framework — 1× in this paper · explained in 2 episodes
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 16 episodes
• Vector Representations — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Linear versus nonlinear forms of partial unconditionality of bases
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
• Electro-optically Modulated Nonlinear Metasurfaces
• Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
• SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
• CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL

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