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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
1 min read
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.
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.”

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

Efficient Schema Filtering Boosts Text-to-SQL Performance

Published:Dec 18, 2025 01:59
1 min read
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.
Reference

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

PermalinkArXiv
Research#llm📝 BlogAnalyzed: Jan 3, 2026 05:57

Text2SQL using Hugging Face Dataset Viewer API and Motherduck DuckDB-NSQL-7B

Published:Apr 4, 2024 00:00
1 min read
Hugging Face

Analysis

The article likely discusses the use of the Hugging Face Dataset Viewer API and Motherduck DuckDB-NSQL-7B for the task of converting natural language text into SQL queries (Text2SQL). This suggests a focus on data access, query generation, and potentially the performance of the NSQL-7B model within the DuckDB environment. The source being Hugging Face indicates a focus on open-source tools and community involvement.
Reference

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PermalinkHugging Face