| Challenge: | Recent work in text-to-SQL has explored toolaugmented LLMs, deep planning, and agentic workflows to address complex challenges. |
| Approach: | They validated a framework for text-to-SQL, Spider 2.0, with 70.2% execution accuracy. |
| Outcome: | The proposed framework achieves 70.2% execution accuracy on a state-of-the-art benchmark for text-to-SQL, Spider 2.0. |
Similar Papers
MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL (2025.coling-main)
Copied to clipboard
Bing Wang, Changyu Ren, Jian Yang, Xinnian Liang, Jiaqi Bai, LinZheng Chai, Zhao Yan, Qian-Wen Zhang, Di Yin, Xing Sun, Zhoujun Li
| Challenge: | Recent LLM-based Text-to-SQL methods suffer from performance degradation on “huge” databases and complex user questions that require multi-step reasoning. |
| Approach: | They propose a framework that integrates a decomposer agent and auxiliary agents to generate SQL queries from natural language text. |
| Outcome: | The proposed framework achieves comparable execution accuracy on SQL-Llama tasks compared to the baseline model. |
Rethinking Text-to-SQL: Dynamic Multi-turn SQL Interaction for Real-world Database Exploration (2026.findings-acl)
Copied to clipboard
Linzhuang Sun, Tianyu Guo, Hao Liang, Ruitong Liu, Yuying Li, Qifeng Cai, Jingxuan Wei, Yuchen Wu, Bihui Yu, Xiangxiang Zhang, Wentao Zhang, Bin Cui
| Challenge: | Structured Query Language (SQL) is the cornerstone for data-driven decision-making. |
| Approach: | They propose a benchmark to rigorously evaluate Large Language Models within a dynamic interaction framework. |
| Outcome: | The proposed benchmark aims to rigorously evaluate LLMs within a dynamic interaction framework. |
SDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probes (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches depend on static, pre-processed database information, which restricts the model’s capacity to deeply comprehend the underlying database content. |
| Approach: | They propose a framework that empowers LLMs to perform Self-Driven Exploration of databases during inference. |
| Outcome: | Evaluated on the BIRD benchmark with Qwen2.5-72B-Instruct, SDE-SQL achieves an 8.02 % improvement in execution accuracy over the baseline. |
DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Recent text-to-SQL systems that use large language models struggle with complex database structures and domain-specific queries. |
| Approach: | a framework that aligns large language models with database knowledge is proposed . DB-Explore constructs database graphs to capture complex relational schemas . |
| Outcome: | a new framework outperforms existing text-to-SQL systems by outperforming existing systems. |
SQL-Trail: Multi-Turn Reinforcement Learning with Interleaved Feedback for Text-to-SQL (2026.acl-long)
Copied to clipboard
Harper Hua, Zhen Han, Zhengyuan Shen, Meng-Chieh Lee, Sheng Guan, Qi Zhu, Sullam Jeoung, Yueyan Chen, Yunfei Bai, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala
| Challenge: | Recent large language models (LLMs) have significantly improved Text-to-SQL generation, but a gap remains between AI systems and human experts on challenging benchmarks such as BIRD-Sql. |
| Approach: | They propose a multi-turn reinforcement learning agentic framework for Text-to-SQL that uses execution feedback to iteratively refine its predictions. |
| Outcome: | The proposed framework outperforms proprietary systems on 7B and 14B models by **5% on average, underscoring the effectiveness of interactive, agentic workflows for robust Text-to-SQL generation. |
PV-SQL: Synergizing Database Probing and Rule-based Verification for Text-to-SQL Agents (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing text-to-SQL systems struggle with deep contextual understanding . |
| Approach: | They propose a framework that provides a tool to help query databases with deeper contextual understanding . they propose two components that iteratively generate probing queries and verify queries . |
| Outcome: | Experiments show PV-SQL outperforms the best text-to-SqL baseline by 5% execution accuracy and 20.8% valid efficiency score while consuming fewer tokens. |
PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are powerful tools for Text-to-SQL tasks . SQL solutions have a relatively fixed pattern, allowing for categorical thinking . |
| Approach: | They propose that query group partitioning allows LLMs to focus on learning the thought processes specific to a single problem type, thus enhancing their reasoning abilities across diverse difficulty levels and problem categories. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Spider and BIRD datasets. |
SQLGenie: A Practical LLM based System for Reliable and Efficient SQL Generation (2025.acl-industry)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) enable natural language to SQL conversion, but generating accurate, efficient queries is challenging due to ambiguous intent, domain knowledge requirements and database constraints. |
| Approach: | They propose a system for reliable SQL generation that integrates Table Onboarder, SQL Generator and Feedback Augmentation. |
| Outcome: | The proposed system surpasses the best single-LLM baseline by 21.5% and the strongest pipeline competitor by 5.3% on public benchmarks and internal datasets. |
CORES: Code-Oriented Reasoning for Complex Text-to-SQL and Generalizable TableQA (2026.findings-acl)
Copied to clipboard
Meng Zhang, Ruochun Jin, Yuanxi Peng, Wenjing Yang, Haotian Wang, Liting Sun, Kun Hu, Silin Yang, Zhang Ke-di
| Challenge: | Text-to-SQL models struggle with complex analytical tasks such as generating simple SQL queries. |
| Approach: | They propose a text-to-sql model that leverages Python as a procedural reasoning pivot to enhance both complex SQL generation and tabular reasoning. |
| Outcome: | The proposed model outperforms baseline models on six text-to-SQL benchmarks by 6.44% on average while maintaining good capability on three tableQA benchmarks. |
Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL (2025.coling-main)
Copied to clipboard
| Challenge: | Existing text-to-SQL approaches have overlooked the critical aspect of system robustness. |
| Approach: | They propose a robust text-to-SQL solution that integrates with LLMs . their method achieves SOTA SQL execution accuracy levels of 82.1% and 58.9% . |
| Outcome: | The proposed solution achieves SOTA SQL execution accuracy levels of 82.1% and 58.9% on the general Spider and Bird benchmarks. |