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SPOC-SQL: Stage-wise Preference Optimization for Controllable Text-to-SQL
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arXiv Computation and Language
Aug 25
SPOC-SQL: Stage-wise Preference Optimization for Controllable Text-to-SQL
SPOC-SQL introduces a stage-wise approach to Text-to-SQL, breaking the task into four sequential subtasks aligned with standard SQL execution logic. It applies fine-grained preference optimization at key decision points and a structured decomposition strategy, enabling explicit intermediate representations for stage-wise intervention and correction. The method yields more controllable and reliable SQL generation, with experiments showing that incorporating stage-wise human knowledge consistently improves performance.
arXiv AI
Jun 16
Integrating Reasoning and Generalization in Text-to-SQL via Self-Enhanced Fine-Tuning
arXiv:2606. 15598v1 Announce Type: new Abstract: Text-to-SQL aims to translate natural language questions into executable SQL queries over structured databases, enabling non-expert users to access data intuitively.
arXiv Machine Learning
Jun 15
Reward-SQL: Boosting Text-to-SQL via Stepwise Execution-Aware Reasoning and Process-Supervised Rewards
arXiv:2505. 04671v3 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) trained with reinforcement learning (RL) have improved Text-to-SQL performance.
arXiv AI
Sep 2
Replacing Training with Memory: Listwise Selection for Text-to-SQL
The paper proposes a fine‑tuning‑free listwise selector for Text‑to‑SQL systems that replaces traditional learning objectives with inference‑time strategies. It introduces reusable structured memories (MaP‑SQL) that encode mappings from natural language to schema elements, SQL operations, and expected outputs, and uses these memories to evaluate candidate queries. To reduce positional bias, the method aggregates rankings across multiple input permutations, optimizing inference cost through execution results and pointwise scoring. The approach achieves higher selection accuracy, fewer unnecessary comparisons, and outperforms the prior state‑of‑the‑art R^3‑SQL on the BIRD‑dev benchmark while using fewer tokens.
arXiv AI
Sep 4
Reflect-SQL: A Self-Reflection Based Framework for Text-to-SQL
Reflect‑SQL is a new framework for converting natural language into SQL queries. It tackles challenges such as large, obscure database schemas, poor table and column retrieval, and syntactically or logically flawed SQL by using a multi‑stage self‑reflection approach. The system iteratively refines queries and SQL through feedback loops driven by an LLM‑as‑a‑judge, achieving 72.03% execution accuracy on the BIRD benchmark, outperforming existing baselines.
arXiv AI
Sep 15
Towards Optimizing SQL Generation via LLM Routing
The paper "Towards Optimizing SQL Generation via LLM Routing" proposes a routing approach for Text-to-SQL tasks that dynamically selects the most cost‑effective large language model (LLM) for each query. Two routing strategies—score‑based and classification‑based—are introduced, achieving accuracy comparable to the best LLM while reducing latency and monetary cost. The authors design the routers for easy training and efficient inference, and demonstrate a practical accuracy‑cost trade‑off on the BIRD dataset.