Innovations Shaping Text-To-SQL Technologies

"𝘞𝘩𝘢𝘵 𝘢𝘳𝘦 𝘵𝘩𝘦 𝘭𝘦𝘷𝘦𝘳𝘴 𝘵𝘰 𝘪𝘮𝘱𝘳𝘰𝘷𝘦 𝘵𝘦𝘹𝘵-𝘵𝘰-𝘚𝘘𝘓 𝘢𝘤𝘤𝘶𝘳𝘢𝘤𝘺?" Text-to-SQL is a foundational building block for enabling AI-assisted workflows in data analytics and science. However, bridging the gap between natural language understanding and the complexity of data schemas requires a multifaceted approach that combines model innovation, data preparation, and user interaction design. Let’s break it down: 𝟭. 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 • Zero-Shot and Few-Shot Learning: Minimal or no task-specific training to enable SQL generation. • Prompt Engineering: Craft tailored prompts with in-context examples and schema hints to improve multi-table join performance. • Reasoning Enhancement: Approaches like Chain of Thought (CoT) and Tree of Thoughts (ToT) improve model accuracy by guiding step-by-step reasoning for complex queries. • Domain-Specific Fine-Tuning: Utilize transfer learning with BERT, TaBERT, and GraPPA to adapt pre-trained language models for schema-specific tasks. • Encoding Innovations: Graph Neural Networks (GNNs), such as RAT-SQL and ShadowGNN, capture schema relationships effectively. Pre-trained Model Adaptations, including SQLova and HydraNet, combine schema features with natural language understanding. • Decoding Techniques: Tree-based decoding and IRNet for intermediate representations. 𝟮. 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 • Schema Grounding: Techniques to align queries with database relationships, and enrich schema embeddings. • Simplification: Normalize schemas to reduce redundancy, or denormalize with pre-joined tables and materialized views for simpler queries. • Abstraction: Provide user-friendly aliases and semantic groupings (e.g., "Customer Data") or organize schema with knowledge graphs. • Metadata Enrichment: Annotate schemas with clear descriptions and summaries to highlight relevant fields. • Partitioning and Contextualization: Divide schemas into smaller subsets and dynamically limit schema visibility based on query intent. • Pre-Computed Views and Data APIs: Create focused views (e.g., “Sales Report”) and prune rarely used columns to streamline model processing. 𝟯. 𝗨𝘀𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗗𝗲𝘀𝗶𝗴𝗻 • Interactive Query Refinement: Implement conversational systems like CoSQL or SParC for iterative query clarification. • Explainability: Provide natural language explanations alongside SQL outputs to increase transparency. • Human-in-the-Loop Validation: Incorporate real-time human review to validate critical queries. • Error Detection and Analysis: Refine outputs with discriminative techniques like Global-GCN and re-ranking to address error patterns systematically. What strategies have you seen work well for text-to-SQL? #AI #DataAnalytics #TextToSQL #MachineLearning #ThoughtLeadership

Alibaba Research Introduces XiYan-SQL: A Multi-Generator Ensemble AI Framework for Text-to-SQL Researchers from Alibaba Group introduced XiYan-SQL, a groundbreaking NL2SQL framework. It integrates multi-generator ensemble strategies and merges the strengths of prompt engineering and SFT. A critical innovation within XiYan-SQL is M-Schema, a semi-structured schema representation method that enhances the system’s understanding of hierarchical database structures. This representation includes key details such as data types, primary keys, and example values, improving the system’s capacity to generate accurate and contextually appropriate SQL queries. This approach allows XiYan-SQL to produce high-quality SQL candidates while optimizing resource utilization. XiYan-SQL employs a three-stage process to generate and refine SQL queries. First, schema linking identifies relevant database elements, reducing extraneous information and focusing on key structures. The system then generates SQL candidates using ICL and SFT-based generators. This ensures diversity in syntax and adaptability to complex queries. Each generated SQL is refined using a correction model to eliminate logical or syntactical errors. Finally, a selection model, fine-tuned to distinguish subtle differences among candidates, selects the best query. XiYan-SQL surpasses traditional methods by integrating these steps into a cohesive and efficient pipeline.... Read the full article here: https://lnkd.in/git5P-xt Paper: https://lnkd.in/g8itpPTH GitHub Page: https://lnkd.in/g3u4aDFh Alibaba Group Alibaba Cloud

Text-to-SQL is a popular GenAI use case, and we recently worked on it with some enterprises. Sharing our learnings here! These enterprises had already tried different approaches—prompting the best LLMs like O1, using RAG with general-purpose LLMs like GPT-4o, and even agent-based methods using AutoGen and Crew. But they hit a ceiling at 85% accuracy, faced response times of over 20 seconds (mainly due to errors from misnamed columns), and dealt with complex engineering that made scaling hard. We found that fine-tuning open-weight LLMs on business-specific query-SQL pairs gave 95% accuracy, reduced response times to under 7 seconds (by eliminating failure recovery), and simplified engineering. These customized LLMs retained domain memory, leading to much better performance. We put together a comparison of all tried approaches. Let me know your thoughts and if you see better ways to approach this - happy to have a 1-1 chat on this. Link in comments.

Text-to-SQL at Pinterest: How LLMs Improved Data Analyst Productivity by 35% 📈 A case study from Pinterest. 🔍📊 Traditional SQL query writing requires extensive knowledge of schema and correct syntax, creating bottlenecks for data analysts in fast-paced environments. 🐌 Pinterest's engineering team tackled this challenge by implementing a Text-to-SQL solution powered by LLMs This solution works in two phases 🏗️: 🔍 Initial Implementation • Users submit analytical questions and select relevant tables  • Table schemas with metadata are retrieved from the data warehouse  • Low-cardinality column values are included to improve accuracy  • The LLM generates SQL code from the natural language question  • Responses are streamed via WebSocket for better user experience 📚 RAG-Enhanced Table Selection • Vector embeddings are created for table summaries and historical queries  • When users don't specify tables, the system finds relevant ones through similarity search  • Table summarization includes descriptions and potential use cases  • Query summarization captures purpose and table relationships  • LLMs help select the most relevant tables from search results This approach achieved a 35% improvement in task completion speed for SQL query writing and increased first-shot acceptance rate from 20% to over 40%. ⬆️ They open-sourced a similar similar architecture called WrenAI – link in the comments 👇

Synthesizing Text-to-SQL Data from Weak and Strong LLMs The paper addresses the challenge of bridging the performance gap between open-source and closed-source large language models (LLMs) in text-to-SQL tasks. Closed-source models, such as GPT-4, have shown significant advancements in natural language processing (NLP) tasks, including text-to-SQL, but they come with concerns regarding openness, privacy, and cost. Open-source models, despite their progress, still lag in performance. The authors propose a novel synthetic data approach that leverages both strong and weak models to enhance domain generalization and improve text-to-SQL models. Key Findings: 🔹 Synthetic Data Approach: ◾ Combining strong data from larger, well-aligned models (e.g., GPT-4) with weak data from smaller, less aligned models significantly enhances domain generalization and model robustness in text-to-SQL tasks. 🔹SENSE Model Performance: ◾The SENSE model achieves state-of-the-art results on the Spider and BIRD benchmarks, surpassing other models, including those based on GPT-4, thus narrowing the performance gap between open-source and closed-source models. 🔹Diversity and Complexity: ◾The synthetic dataset generated by strong models exhibits higher domain diversity and complexity, improving the model’s ability to handle cross-domain text-to-SQL queries. 🔹Preference Learning: ◾Using weak data for preference learning helps reduce errors and hallucinations in SQL generation, enhancing the model's precision in distinguishing correct from incorrect SQL queries. 🔹Robustness: ◾SENSE shows superior performance on robustness benchmarks (SYN, REALISTIC, DK), indicating its effectiveness in handling diverse and challenging scenarios. 🔹Ablation Study: ◾Both strong and weak data are crucial for optimal performance. Strong data boosts domain generalization, while weak data refines error handling and reduces hallucinations. 🔹Transferability: ◾The synthetic data approach is effective across different LLMs, demonstrating its broad applicability and potential for general use in various text-to-SQL tasks. The paper concludes that synthesizing text-to-SQL data from both weak and strong LLMs significantly enhances the performance and robustness of text-to-SQL models. The release of SENSE data and models aims to further the progress in the text-to-SQL domain, highlighting the potential of open-source LLMs when fine-tuned with synthetic data. #GenAI #LLM #AI #Datascience #Machinelearning Reference : https://lnkd.in/graArkRd

💡Current Text-to-SQL methods might not be good enough for real-world enterprise data, according to this new paper. The paper also proposes a new method called TAG (Table-Augmented Generation) to address this issue 📖 Insights 👉 Text2SQL and Retrieval-Augmented Generation (RAG) methods are insufficient for many real-world business queries because they fail to handle complex reasoning that combines domain knowledge, world knowledge, exact computation, and semantic reasoning. 👉 Text2SQL is limited to queries that can be directly translated into SQL, missing out on a broader range of natural language queries that require more advanced reasoning. 👉 RAG is constrained by its reliance on point lookups and single LM invocations, which do not leverage the full computational capabilities of databases and are prone to errors, especially with long-context prompts. 👉 TAG introduces a unified approach that combines database systems and LMs to address complex natural language queries. It involves three steps: query synthesis, query execution, and answer generation. 👉 TAG can handle a broader range of queries by combining the computational power of databases with the reasoning capabilities of LMs. It also unifies and extends the capabilities of both Text2SQL and RAG. 👉 TAG systems have shown significantly higher accuracy (up to 65% better) compared to existing methods, indicating their potential to transform how users interact with data. Link: https://lnkd.in/e7eC9m_T

Researchers from UC Berkeley and Stanford University propose 𝐓𝐚𝐛𝐥𝐞-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝐓𝐀𝐆), 𝐚 𝐧𝐞𝐰 𝐩𝐚𝐫𝐚𝐝𝐢𝐠𝐦 𝐟𝐨𝐫 𝐚𝐧𝐬𝐰𝐞𝐫𝐢𝐧𝐠 𝐧𝐚𝐭𝐮𝐫𝐚𝐥 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐨𝐯𝐞𝐫 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬. TAG introduces a unified approach involving three steps: translating the user's query into an executable database query (query synthesis), running this query to retrieve relevant data (query execution), and using this data along with the query to generate a natural language answer (answer generation). Unlike Text2SQL and RAG, which are limited to specific cases, TAG addresses a broader range of queries. 𝐈𝐧𝐢𝐭𝐢𝐚𝐥 𝐛𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐬 𝐬𝐡𝐨𝐰 𝐭𝐡𝐚𝐭 𝐞𝐱𝐢𝐬𝐭𝐢𝐧𝐠 𝐦𝐞𝐭𝐡𝐨𝐝𝐬 𝐚𝐜𝐡𝐢𝐞𝐯𝐞 𝐥𝐞𝐬𝐬 𝐭𝐡𝐚𝐧 20% 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲, 𝐰𝐡𝐢𝐥𝐞 𝐓𝐀𝐆 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧𝐬 𝐜𝐚𝐧 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐛𝐲 20-65%, 𝐡𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐢𝐧𝐠 𝐢𝐭𝐬 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥. SOURCE: https://lnkd.in/gdt9t8wX CODE: https://lnkd.in/gHvUbehX

3️⃣ Takeaways from the Latest Research on Text-to-SQL 🧠 🚫 1. Skip Schema Linking Schema linking, which maps user queries to relevant tables and columns, isn't needed anymore. Research from Distyl AI shows that with powerful LLMs, you can skip this step entirely. Instead, simply pass all the info directly to the model—it’s capable of identifying relevant information on its own. 💡 2. Generate Multiple Candidate Answers The CHASE-SQL paper from Google recommends experimenting with different paths for SQL generation, like Query Plan, Divide and Conquer, and Synthetic Examples. By creating a diverse set of candidates, you can boost accuracy. The key insight here: LLMs have the knowledge and ability to write SQL—you just need to coax it out of them. 🎯 3. Fine-Tuned Models for Candidate Selection In CHASE-SQL, a fine-tuned model serves as a "candidate selector," using binary classification to pick the best SQL query. Fine-tuning enhances the model’s grasp of data nuances, leading to significantly better results. While an extra step, it can noticeably improve performance. 🐦 About BIRD-SQL BIRD-SQL is one of the most practical text-to-SQL benchmarks, featuring 12,751 real user questions spanning 95 databases across multiple industries. It's one of the toughest public benchmarks for text-to-SQL.