Search: text-to-SQL - ai.jp.net

Research Paper #Text-to-SQL, Reinforcement Learning, Data Synthesis 🔬 ResearchAnalyzed: Jan 3, 2026 18:56

AGRO-SQL: Agentic RL for Text-to-SQL

Published:Dec 29, 2025 10:49

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1 min read

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ArXiv

Analysis

This paper addresses the limitations of Text-to-SQL systems by tackling the scarcity of high-quality training data and the reasoning challenges of existing models. It proposes a novel framework combining data synthesis and a new reinforcement learning approach. The data-centric approach focuses on creating high-quality, verified training data, while the model-centric approach introduces an agentic RL framework with a diversity-aware cold start and group relative policy optimization. The results show state-of-the-art performance, indicating a significant contribution to the field.

Key Takeaways

•Proposes AGRO-SQL, a novel framework for Text-to-SQL.
•Employs a dual-centric approach: data-centric (data synthesis) and model-centric (agentic RL).
•Introduces a Diversity-Aware Cold Start and Group Relative Policy Optimization (GRPO) for the RL agent.
•Achieves state-of-the-art performance on BIRD and Spider benchmarks.

Reference

“The synergistic approach achieves state-of-the-art performance among single-model methods.”

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Research #llm 📝 BlogAnalyzed: Dec 28, 2025 15:02

Automating Ad Analysis: Potential of Agentic BI and Data Infrastructure

Published:Dec 28, 2025 14:42

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1 min read

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Qiita AI

Analysis

This article discusses the limitations of Text-to-SQL in practical data analysis, particularly in the context of advertising, and explores the potential of "Agentic BI" as a solution. It highlights the growing expectation for natural language queries in data analysis driven by advancements in generative AI. The article likely delves into how Agentic BI can overcome the shortcomings of Text-to-SQL by providing a more comprehensive and automated approach to ad analysis. It suggests that while Text-to-SQL has promise, it may not be sufficient for complex real-world scenarios, paving the way for more sophisticated AI-powered solutions like Agentic BI. The focus on data infrastructure implies the importance of a robust foundation for effective AI-driven analysis.

Key Takeaways

•Text-to-SQL has limitations in real-world ad analysis.
•Agentic BI offers a more comprehensive solution for automated ad analysis.
•Robust data infrastructure is crucial for effective AI-driven analysis.

Reference

“"自然言語によるクエリ（Text-to-SQL）」への期待が高まっています。"”

Permalink Qiita AI

Paper #Text-to-SQL, Semantic Validation, Natural Language Processing, AI 🔬 ResearchAnalyzed: Jan 3, 2026 19:39

Hierarchical Representation for Semantic Validation in Text-to-SQL

Published:Dec 28, 2025 02:25

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1 min read

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ArXiv

Analysis

This paper addresses the critical problem of semantic validation in Text-to-SQL systems, which is crucial for ensuring the reliability and executability of generated SQL queries. The authors propose a novel hierarchical representation approach, HEROSQL, that integrates global user intent (Logical Plans) and local SQL structural details (Abstract Syntax Trees). The use of a Nested Message Passing Neural Network and an AST-driven sub-SQL augmentation strategy are key innovations. The paper's significance lies in its potential to improve the accuracy and interpretability of Text-to-SQL systems, leading to more reliable data querying platforms.

Key Takeaways

Reference

“HEROSQL achieves an average 9.40% improvement of AUPRC and 12.35% of AUROC in identifying semantic inconsistencies.”

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Research Paper #Text-to-SQL, LLM, Cloud Computing Costs 🔬 ResearchAnalyzed: Jan 3, 2026 20:08

Cost-Aware Text-to-SQL: Cloud Compute Cost Analysis for LLM-Generated Queries

Published:Dec 26, 2025 19:51

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1 min read

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ArXiv

Analysis

This paper addresses a critical gap in evaluating Text-to-SQL systems by focusing on cloud compute costs, a more relevant metric than execution time for real-world deployments. It highlights the cost inefficiencies of LLM-generated SQL queries and provides actionable insights for optimization, particularly for enterprise environments. The study's focus on cost variance and identification of inefficiency patterns is valuable.

Key Takeaways

•Execution time is a poor indicator of query cost.
•LLM-generated queries can exhibit significant cost variance.
•Inefficiency patterns like missing partition filters and full-table scans are prevalent.
•Reasoning models can be more cost-effective than standard models.

Reference

“Reasoning models process 44.5% fewer bytes than standard models while maintaining equivalent correctness.”

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 10:30

Hallucination Detection for LLM-based Text-to-SQL Generation via Two-Stage Metamorphic Testing

Published:Dec 24, 2025 04:04

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1 min read

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ArXiv

Analysis

The article focuses on a critical problem in LLM applications: the generation of incorrect or fabricated information (hallucinations) in the context of Text-to-SQL tasks. The proposed solution utilizes a two-stage metamorphic testing approach. This suggests a focus on improving the reliability and accuracy of LLM-generated SQL queries. The use of metamorphic testing implies a method of checking the consistency of the LLM's output under various transformations of the input, which is a robust approach to identify potential errors.

Key Takeaways

•Addresses the problem of hallucinations in LLM-generated SQL.
•Proposes a two-stage metamorphic testing approach.
•Aims to improve the reliability and accuracy of Text-to-SQL generation.

Reference

“The article likely presents a novel method for detecting and mitigating hallucinations in LLM-based Text-to-SQL generation.”

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 10:02

Multi-agent Text2SQL Framework with Small Language Models and Execution Feedback

Published:Dec 21, 2025 06:43

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1 min read

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ArXiv

Analysis

This article describes a research paper on a Text-to-SQL framework. The use of multi-agent systems and execution feedback with small language models suggests an approach focused on efficiency and potentially improved accuracy. The source being ArXiv indicates this is a preliminary research finding.

Key Takeaways

Reference

“The article likely details the architecture of the multi-agent system, the specific small language models used, and the feedback mechanisms employed. It would also likely include experimental results and comparisons to existing Text-to-SQL methods.”

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Research #Text-to-SQL 🔬 ResearchAnalyzed: Jan 10, 2026 09:36

Identifying Unanswerable Questions in Text-to-SQL Tasks

Published:Dec 19, 2025 12:22

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1 min read

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ArXiv

Analysis

This research from ArXiv likely focuses on improving the reliability of Text-to-SQL systems by identifying queries that cannot be answered based on the provided data. This is a crucial step towards building more robust and trustworthy AI applications that interact with data.

Key Takeaways

•Focuses on improving the accuracy and reliability of Text-to-SQL systems.
•Addresses the problem of handling questions that cannot be answered.
•Potentially involves techniques for analyzing the semantic content of questions and the structure of the database.

Reference

“The research likely explores methods to detect when a natural language question cannot be translated into a valid SQL query.”

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 08:03

Knowledge Distillation with Structured Chain-of-Thought for Text-to-SQL

Published:Dec 18, 2025 20:41

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1 min read

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ArXiv

Analysis

This article likely presents a novel approach to improving Text-to-SQL models. It combines knowledge distillation, a technique for transferring knowledge from a larger model to a smaller one, with structured chain-of-thought prompting, which guides the model through a series of reasoning steps. The combination suggests an attempt to enhance the accuracy and efficiency of SQL generation from natural language queries. The use of ArXiv as the source indicates this is a research paper, likely detailing the methodology, experiments, and results of the proposed approach.

Key Takeaways

•Focuses on improving Text-to-SQL models.
•Employs knowledge distillation and structured chain-of-thought.
•Aims to enhance accuracy and efficiency of SQL generation.
•Likely a research paper from ArXiv.

Reference

“The article likely explores how to improve the performance of Text-to-SQL models by leveraging knowledge from a larger model and guiding the reasoning process.”

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Research #Text2SQL 🔬 ResearchAnalyzed: Jan 10, 2026 10:12

Efficient Schema Filtering Boosts Text-to-SQL Performance

Published:Dec 18, 2025 01:59

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1 min read

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ArXiv

Analysis

This research explores improving the efficiency of Text-to-SQL systems. The use of functional dependency graph rerankers for schema filtering presents a novel approach to optimize LLM performance in this domain.

Key Takeaways

•Focuses on improving the efficiency of Text-to-SQL systems.
•Employs schema filtering techniques to optimize LLM performance.
•Uses Functional Dependency Graph Rerankers.

Reference

“The article's source is ArXiv, indicating a research paper.”

Permalink ArXiv

Research #Database 🔬 ResearchAnalyzed: Jan 10, 2026 10:41

DAR: Autonomous Database Exploration Revolutionizes Data Analysis

Published:Dec 16, 2025 17:36

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1 min read

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ArXiv

Analysis

The paper likely presents a novel approach to database exploration, moving beyond text-to-SQL limitations. This could lead to more efficient and insightful data analysis by automating complex queries and research processes.

Key Takeaways

•The research focuses on autonomous database exploration.
•The system potentially goes beyond text-to-SQL approaches.
•The use of "DAR" suggests a novel research project.

Reference

“The article's context indicates the research is presented on ArXiv, suggesting it's a preliminary publication.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 10:29

FloodSQL-Bench: A Retrieval-Augmented Benchmark for Geospatially-Grounded Text-to-SQL

Published:Dec 12, 2025 23:25

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1 min read

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ArXiv

Analysis

The article introduces FloodSQL-Bench, a new benchmark designed for evaluating Text-to-SQL models that incorporate geospatial information. This suggests a focus on improving the ability of language models to understand and process queries related to location data. The use of 'retrieval-augmented' implies the benchmark likely tests models that leverage external knowledge sources to answer questions.

Key Takeaways

Reference

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Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:18

Beyond Query-Level Comparison: Fine-Grained Reinforcement Learning for Text-to-SQL with Automated Interpretable Critiques

Published:Nov 27, 2025 09:33

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1 min read

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ArXiv

Analysis

The article likely presents a novel approach to Text-to-SQL tasks, moving beyond simple query-level comparisons. It focuses on fine-grained reinforcement learning and incorporates automated, interpretable critiques to improve performance and understanding of the model's behavior. The use of reinforcement learning suggests an attempt to optimize the model's output directly, rather than relying solely on supervised learning. The emphasis on interpretability is crucial for understanding the model's decision-making process and identifying potential biases or errors.

Key Takeaways

Reference

“”

Permalink ArXiv

Research #Text-to-SQL 🔬 ResearchAnalyzed: Jan 10, 2026 14:13

Text-to-SQL Advances: Dual-State Reasoning for Improved Context and Generation

Published:Nov 26, 2025 13:52

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1 min read

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ArXiv

Analysis

This ArXiv paper explores a novel approach to the Text-to-SQL task, focusing on dual-state reasoning to enhance both context understanding and SQL query generation. The research likely contributes to advancements in natural language processing and database interaction.

Key Takeaways

•Focuses on Text-to-SQL, bridging natural language and database queries.
•Employs dual-state reasoning, likely improving accuracy and efficiency.
•The research originates from an ArXiv paper, indicating ongoing academic exploration.

Reference

“The paper presents a dual-state reasoning approach.”

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 10:15

AutoLink: Autonomous Schema Exploration and Expansion for Scalable Schema Linking in Text-to-SQL at Scale

Published:Nov 21, 2025 12:12

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1 min read

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ArXiv

Analysis

This article introduces AutoLink, a system designed to improve schema linking in Text-to-SQL tasks. The focus is on scalability and autonomous exploration and expansion of schemas. The research likely explores methods to efficiently link natural language queries to database schemas, which is a crucial step in converting text into SQL queries. The 'at scale' aspect suggests the system is designed to handle large datasets and complex schemas.

Key Takeaways

Reference

“”

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Research #Text-to-SQL 🔬 ResearchAnalyzed: Jan 10, 2026 14:41

New Benchmark for Text-to-SQL Translation Focuses on Real-World Complexity

Published:Nov 17, 2025 16:52

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1 min read

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ArXiv

Analysis

This research introduces a novel benchmark for Text-to-SQL translation, going beyond simplistic SELECT statements. This advancement is crucial for improving the practicality and applicability of AI in data interaction.

Key Takeaways

•The benchmark addresses complexities beyond basic SQL queries.
•It likely uses a taxonomy to categorize and evaluate different SQL query types.
•The focus is on improving real-world text-to-SQL performance.

Reference

“The research focuses on creating a comprehensive taxonomy-guided benchmark.”

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 08:17

Prompt Engineering Techniques for Context-dependent Text-to-SQL in Arabic

Published:Nov 16, 2025 00:05

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1 min read

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ArXiv

Analysis

This article likely explores methods to improve the performance of Large Language Models (LLMs) in converting Arabic text into SQL queries, focusing on techniques like prompt engineering. The context-dependent aspect suggests the research addresses the challenges of understanding and incorporating surrounding information within the Arabic text to generate accurate SQL queries. The source, ArXiv, indicates this is a research paper.

Key Takeaways

Reference

“”

Permalink ArXiv

Research #Text-to-SQL 👥 CommunityAnalyzed: Jan 10, 2026 15:46

Natural-SQL-7B: A New Text-to-SQL Model Emerges

Published:Feb 5, 2024 14:22

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1 min read

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Hacker News

Analysis

The article announces the release of Natural-SQL-7B, a text-to-SQL model, likely highlighting its performance or unique features. Further details on its capabilities, benchmarks, and potential impact are crucial for a complete understanding.

Key Takeaways

•A new text-to-SQL model, Natural-SQL-7B, has been announced.
•The announcement originates from Hacker News, suggesting it's potentially an early release or project.
•The article implies a focus on the model's strength, indicating a competitive offering.

Reference

“Natural-SQL-7B is a strong text-to-SQL model.”

Permalink Hacker News

Research #Text-to-SQL 👥 CommunityAnalyzed: Jan 10, 2026 15:47

Open Source Text-to-SQL LLM for DuckDB

Published:Jan 25, 2024 17:08

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1 min read

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Hacker News

Analysis

The article likely discusses a new open-source project that utilizes a large language model to translate natural language into SQL queries for DuckDB. This could potentially lower the barrier to entry for data analysis by allowing users to interact with databases more intuitively.

Key Takeaways

•An open-source text-to-SQL LLM is being developed.
•The LLM specifically targets DuckDB.
•This may improve accessibility to database querying.

Reference

“An open source DuckDB text to SQL LLM”

Permalink Hacker News

AGRO-SQL: Agentic RL for Text-to-SQL

Analysis

Key Takeaways

Automating Ad Analysis: Potential of Agentic BI and Data Infrastructure

Analysis

Key Takeaways

Hierarchical Representation for Semantic Validation in Text-to-SQL

Analysis

Key Takeaways

Cost-Aware Text-to-SQL: Cloud Compute Cost Analysis for LLM-Generated Queries

Analysis

Key Takeaways

Hallucination Detection for LLM-based Text-to-SQL Generation via Two-Stage Metamorphic Testing

Analysis

Key Takeaways

Multi-agent Text2SQL Framework with Small Language Models and Execution Feedback

Analysis

Key Takeaways

Identifying Unanswerable Questions in Text-to-SQL Tasks

Analysis

Key Takeaways

Knowledge Distillation with Structured Chain-of-Thought for Text-to-SQL

Analysis

Key Takeaways

Efficient Schema Filtering Boosts Text-to-SQL Performance

Analysis

Key Takeaways

DAR: Autonomous Database Exploration Revolutionizes Data Analysis

Analysis

Key Takeaways

FloodSQL-Bench: A Retrieval-Augmented Benchmark for Geospatially-Grounded Text-to-SQL

Analysis

Key Takeaways

Beyond Query-Level Comparison: Fine-Grained Reinforcement Learning for Text-to-SQL with Automated Interpretable Critiques

Analysis

Key Takeaways

Text-to-SQL Advances: Dual-State Reasoning for Improved Context and Generation

Analysis

Key Takeaways

AutoLink: Autonomous Schema Exploration and Expansion for Scalable Schema Linking in Text-to-SQL at Scale

Analysis

Key Takeaways

New Benchmark for Text-to-SQL Translation Focuses on Real-World Complexity

Analysis

Key Takeaways

Prompt Engineering Techniques for Context-dependent Text-to-SQL in Arabic

Analysis

Key Takeaways

Natural-SQL-7B: A New Text-to-SQL Model Emerges

Analysis

Key Takeaways

Open Source Text-to-SQL LLM for DuckDB

Analysis

Key Takeaways

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