Summary Text-to-SQL Parsing for Large-Scale Databases arxiv.org
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The article discusses the development of the BIRD dataset, a benchmark for text-to-SQL parsing of large-scale databases, which investigates the complexity and diversity of SQLs used in real-world scenarios with large database contents and emphasizes the need for text-to-SQL models to feature database value comprehension in addition to semantic parsing.
Key Points
- BIRD is a big benchmark for large-scale databases grounded in text-to-SQL tasks.
- The BIRD dataset was created by acquiring open tables from various sources, synthesizing and standardizing schemas, and generating database relational data.
- The study evaluates text-to-SQL parsers using two metrics, execution accuracy and valid efficiency score, and provides a distribution analysis of SQLs across four dimensions.
- The BIRD benchmark investigates the complexity and diversity of SQLs used in real-world scenarios with large database contents, highlighting the importance of understanding database values and time-sensitive data.
- The authors compare baseline models and propose evaluation metrics to measure efficiency and accuracy of SQL predictions.
- The document discusses text-to-SQL parsing for large-scale databases and several challenges that need to be addressed, including handling external knowledge, large and dirty database values, optimizing SQL execution efficiency, and reasoning.
Summaries
268 word summary
This document discusses text-to-SQL parsing for large-scale databases, including challenges such as handling external knowledge and optimizing SQL execution efficiency. The authors provide efficiency and error analyses, as well as SQL optimization techniques. They highlight the need for more advanced and practical text-to-SQL solutions in real-world scenarios. The article also discusses the use of pre-trained language models, proposing evaluation metrics to measure efficiency and accuracy of SQL predictions. They present the development of a text-to-SQL system called BIRD, which includes an embodied database that considers value descriptions of databases while generating SQLs. The authors propose the novel sub-task of text-to-efficient-SQL and suggest the integration of different sources of knowledge as a promising future direction for innovation. This article discusses the development of the BIRD dataset, a benchmark for text-to-SQL parsing of large-scale databases. The dataset was created by acquiring open tables from various sources and generating database relational data. The BIRD benchmark investigates the complexity and diversity of SQLs used in real-world scenarios with large database contents. It contains questions with two main categories, Fundamental Type and Reasoning Type, and requires domain knowledge and numeric reasoning. The study emphasizes the need for text-to-SQL models to feature database value comprehension in addition to semantic parsing. The proposed workflow involves specialists assembling and producing databases and description files, experts teaching and evaluating crowdsourcing people, and SQL annotators producing SQL files equipped with databases. The BIRD dataset is compared to other cross-domain text-to-SQL benchmarks, with detailed descriptions of database values provided to aid comprehension. It provides a more comprehensive difficulty analysis on the three difficulty levels, namely simple, moderate, and challenging.
807 word summary
This article discusses the challenges of achieving high execution accuracy in text-to-SQL parsing for large-scale databases. The study emphasizes the need for text-to-SQL models to feature database value comprehension in addition to semantic parsing. To address the challenges of dirty database contents and external knowledge between NL questions, the study presents BIRD, a big benchmark for large-scale databases grounded in text-to-SQL tasks. The article proposes a benchmark called BIRD for text-to-SQL parsing in large-scale databases, which includes various databases and requires external knowledge and reasoning for accurate results. The benchmark also considers execution efficiency and noisy data values. The proposed workflow involves specialists assembling and producing databases and description files, experts teaching and evaluating crowdsourcing people, and SQL annotators producing SQL files equipped with databases. This document discusses the development of the BIRD dataset for text-to-SQL parsing of large-scale databases. The dataset was created by acquiring open tables from various sources, synthesizing and standardizing schemas, and generating database relational data. The study employs a double-blind technique to qualify annotators for SQL query annotation, with the team composed of skilled data engineers and database students. The BIRD dataset is compared to other cross-domain text-to-SQL benchmarks, with detailed descriptions of database values provided to aid comprehension. The evaluation process involves examining each data in two dimensions: SQL validness and text-knowledge-SQL alignment. The BIRD dataset is a large-scale cross-domain benchmark for text-to-SQL parsing, containing two macro-categories and covering a variety of domains. It provides a more comprehensive difficulty analysis on the three difficulty levels, namely simple, moderate, and challenging. The study evaluates text-to-SQL parsers using two metrics, execution accuracy and valid efficiency score, and provides a distribution analysis of SQLs across four dimensions. The BIRD benchmark investigates the complexity and diversity of SQLs used in real-world scenarios with large database contents, highlighting the importance of understanding database values and time-sensitive data. The evaluation set for text-to-SQL parsing contains questions with two main categories, Fundamental Type and Reasoning Type, with Execution Accuracy defined as the proportion of questions with identical results between predicted and ground-truth inquiries. The BIRD questions require domain knowledge and numeric reasoning, and examples include computation, numeric, synonym, and knowledge domain types. This article discusses text-to-SQL parsing for large-scale databases using pre-trained language models. The authors compare baseline models and propose evaluation metrics to measure efficiency and accuracy of SQL predictions. They analyze SQL queries in the BIRD dataset and investigate the impact of multi-step reasoning of LLMs on BIRD. The article presents an SQL query example and mentions the importance of external knowledge. The development of a text-to-SQL system called BIRD is discussed, which includes an embodied database that considers value descriptions of databases while generating SQLs. The system's effectiveness is evaluated using metrics, and the authors propose the novel sub-task of text-to-efficient-SQL. They introduce the LLM plugin for integrating different sources of knowledge and suggest that this presents a promising future direction for innovation. This document discusses text-to-SQL parsing for large-scale databases and several challenges that need to be addressed, including handling external knowledge, large and dirty database values, optimizing SQL execution efficiency, and reasoning. The article identifies three main errors that ChatGPT can make during the parsing process, provides an error analysis of ChatGPT's performance, and includes a human performance benchmark for comparison. The BIRD benchmark is presented, which is more challenging than existing benchmarks and leaves plenty of room for improvement and innovation. The authors also provide efficiency and error analyses, which offer valuable insights and directions for future research. SQL optimization techniques such as adding indexes to a database and utilizing the COUNT function on a NOT-NULL column can increase time efficiency. The document highlights the need for more advanced and practical text-to-SQL solutions in real-world scenarios. Paragraph 1: A study presented at the Joint Conference on Natural Language Processing aimed to improve text-to-SQL models' robustness against synonym substitution. Another study used the ATIS-3 corpus to focus on knowledge-intensive text-to-SQL semantic parsing, and the FinQA dataset was presented for numerical reasoning over financial data.
Paragraph 2: The article discusses the development of a text-to-SQL parser for large databases, referencing various studies and datasets used to improve accuracy and efficiency. The goal is to create a parser that can handle complex queries across different domains.
Paragraph 3: This is a list of references and resources related to text-to-SQL parsing for large-scale databases, covering topics such as semantic parsing, language models, neural networks, and query optimization.
Paragraph 4: This is a list of references related to text-to-SQL parsing, including research papers and conference proceedings. Some notable papers include DuSQL and RAT-SQL.
Paragraph 5: This excerpt is a list of references to research papers and conference proceedings related to text-to-SQL parsing for large-scale databases. The references cover topics such as machine learning algorithms, query rewrite systems, pre-trained transformer language models, and human-labeled datasets for semantic parsing.
2598 word summary
This excerpt is a list of references to research papers and conference proceedings related to text-to-SQL parsing for large-scale databases. The references cover topics such as machine learning algorithms, query rewrite systems, pre-trained transformer language models, and human-labeled datasets for semantic parsing. They also include specific papers on grounded adaptation for zero-shot natural language, executable semantic parsing, and type-aware neural text-to-SQL generation. This is a list of references related to text-to-SQL parsing, including research papers and conference proceedings. The references cover various topics such as query synthesis, generating structured queries from natural language, and tasking structured knowledge grounding with text-to-text language models. Some notable papers include DuSQL, a large-scale Chinese text-to-SQL dataset, and RAT-SQL, a neural network model that can generate SQL queries from natural language questions. This is a list of references and resources related to text-to-SQL parsing for large-scale databases. It includes papers and articles on topics such as semantic parsing, language models, neural networks, and query optimization. Some key papers mentioned include PICARD, RASAT, and DIN-SQL, which explore different approaches to text-to-SQL parsing using techniques such as incremental parsing, relational structures, and in-context learning. Other papers discuss the limitations of AI and the challenges of handling natural language variation. The list also includes papers on related topics such as question answering and transfer learning. This document provides a list of references related to text-to-SQL parsing for large-scale databases. The references cover topics such as incremental data cleaning, probabilistic approach for NLP based query processing, rule-based approach for text-to-SQL parsing, mixing pre-trained transformers with graph-aware layers, SQL query optimization methods of relational database system, and more. The document also includes a practical text-to-SQL benchmark for electronic health records and an experimental evaluation of index selection algorithms. Additionally, it features a survey on data cleaning and a study on how language models are zero-shot reasoners. The article discusses the development of a text-to-SQL parser for large databases. It references various studies and datasets that have been used to improve the accuracy and efficiency of such parsers, including the QASC dataset and a neural semantic parser. The article also mentions trends in cleaning relational data and the use of pre-trained language models. The goal is to create a parser that can handle complex queries across different domains. A study on text-to-SQL parsing for large-scale databases was presented at the Joint Conference on Natural Language Processing. The study aimed to improve the robustness of text-to-SQL models against synonym substitution. Another study focused on knowledge-intensive text-to-SQL semantic parsing, using the ATIS-3 corpus. A dataset called FinQA was also presented, which uses large language models trained on code to perform numerical reasoning over financial data. Several authors and editors were involved in these studies, with their names and affiliations listed. This document discusses text-to-SQL parsing for large-scale databases and several challenges that need to be addressed, including handling external knowledge, large and dirty database values, optimizing SQL execution efficiency, and reasoning. The authors present the BIRD benchmark, which is more challenging than existing benchmarks and leaves plenty of room for improvement and innovation. They also provide efficiency and error analyses, which offer valuable insights and directions for future research. The paper highlights the need for more advanced and practical text-to-SQL solutions in real-world scenarios. BIRD is a large-scale cross-domain text-to-SQL benchmark. The trade-off between efficiency and execution accuracy should be explored in future research. VES is provided to measure efficiency of text-to-SQL generators. SQL optimization is a common method for enhancing the efficacy of SQL queries. Several SQL optimization algorithms have been proved effective. LLMs such as ChatGPT, Palm, OPT, and RASAT have achieved state-of-the-art results on complicated cross-domain text-to-SQL tasks. A cross-domain text-to-SQL parser involves an encoder and decoder to generate SQLs. The BIRD is the first large-scale benchmark to incorporate all of the aforementioned real-world features, with a particular emphasis on prioritizing the database values. Recent datasets, such as EHRSQL, SEDE, and MIMICSQL, collected databases with diverse and large values. Despite these improvements, the majority of cross-domain text-to-SQL datasets continue to focus on the database schema as opposed to the database values. Four major types of error cases are presented in Figure 8. Text-to-SQL parsing is a method for converting natural language to SQL queries. The dataset used for this method is important, and datasets like WikiSQL and Spider are appropriate. There are also ways to optimize SQL efficiency by using indexes in databases. This document discusses various optimization techniques for SQL queries to improve efficiency and save time. Adding indexes to a database and utilizing the COUNT function on a NOT-NULL column can increase time efficiency. Applying a JOIN operation instead of a subquery with IN can also improve efficiency. ChatGPT, a language model, is occasionally prone to syntax errors and may replicate formulas without considering SQL syntax, potentially exposing the system to data security risks. The article discusses the challenges of text-to-SQL parsing for large-scale databases and identifies three main errors that ChatGPT, a code-based large language model, can make during the parsing process. These errors include misunderstanding knowledge evidence, misunderstanding database content, and schema linking. The article provides an error analysis of ChatGPT's performance and also includes a human performance benchmark for comparison. The article concludes with a discussion of how to improve SQL query efficiency by setting indexes in the database. The paper discusses the development of a text-to-SQL system called BIRD, which includes an embodied database that considers value descriptions of databases while generating SQLs. The system has two sub-procedures, semantic parsing and SQL optimization, with the latter consisting of two stages. The system's effectiveness is evaluated using the Valid Efficiency Score (VES) and Execution Accuracy (EX) metrics. The authors propose the novel sub-task of text-to-efficient-SQL and investigate two prospective approaches for enhancing text-to-SQL systems to generate more efficient SQL queries. They also introduce the LLM plugin for integrating different sources of knowledge. The results show that the system achieves higher VES and EX scores than SOTA models, and the authors suggest that this presents a promising future direction for innovation. This article discusses Text-to-SQL parsing for large-scale databases using coherently reasoning knowledge with external LLMs. The article presents an SQL query to find the consumption status of people in August 2012, filter people who paid more than $29.00, and calculate the price per unit of product ID No.5. The article also mentions the importance of external knowledge and provides a detailed prompt design for implementing ChatGPT + KG + COT. Additionally, the article discusses the performance of different models with and without external knowledge and concludes that external knowledge is beneficial for text-to-SQL models when facing more real cases. Finally, the article includes tables and SQL outputs for better understanding. The document discusses text-to-SQL parsing for large-scale databases. The final SQL is generated step by step, and external knowledge is used to answer questions about the database. The tables provided above are used, and valid SQLite and external knowledge are necessary. Singer, gas stations, and customers tables are created using DDL prompts. The SQL is filtered by birth year and nation, and the singers are counted. External knowledge is provided by annotators for execution accuracy analysis, and the impact of multi-step reasoning of LLMs on BIRD is investigated. A 1-shot pseudo example for ChatGPT is given, and the COT technique is used with the prompt sentence "Let's think step by step." Programming-based prompts are used, and models are not allowed to see unseen databases without additional training. The paper discusses text-to-SQL parsing for large-scale databases using pre-trained language models (LLMs) such as Codex and ChatGPT. The authors compare two types of baseline models, one based on in-context learning (ICL) and the other based on fine-tuning (FT) techniques. They propose two evaluation metrics, Valid Efficiency Score (VES) and Execution Time Ratio (ETR), to measure the efficiency and accuracy of SQL predictions. The authors also analyze the SQL queries in the BIRD dataset and other cross-domain text-to-SQL benchmarks. The document presents a comprehensive database distribution in the BIRD, including domain and size, as well as database value type distribution. The evaluation set for text-to-SQL parsing contains questions with two main categories: Fundamental Type and Reasoning Type. Execution Accuracy is defined as the proportion of questions in the evaluation set for which the execution results of both the predicted and ground-truth inquiries are identical. The Reasoning Type of questions requires external knowledge grounding to answer. The questions in the BIRD are comparable to other text-to-SQL benchmarks. The text includes examples of SQL queries and their corresponding questions, including computation, numeric, synonym, and knowledge domain types, with their respective illustration, value, synonym, computation, and knowledge domains. The Text-to-SQL Parsing for Large-Scale Databases document provides insights into the complexity and diversity of SQLs used in the BIRD benchmark. The BIRD investigates the distribution of database domains, database size, and value types. It indicates that more real-world questions in text-to-SQL applications demand a thorough understanding of database values, which is consistent with their motivation for creating the BIRD benchmark. The prevalence of datetime expressions and their related questions in the dataset highlights the importance of time-sensitive data in real-world applications. The study also shows that there are ample examples that require domain knowledge and numeric reasoning, making BIRD questions especially challenging. Among the Reasoning Type of questions, referring to tables or columns continues to be the most important topic.
In terms of specific examples, one question asks how many gas stations in CZE have Premium gas, with a 83.9% percentage match-based fundamental question type. Another question asks for the titles of the top 5 posts with the highest popularity, with a 20.3% ranking question type. A third question asks how many color cards with no borders have been ranked higher than 12000 on EDHRec, with a 16.7% comparison question type. Finally, a fourth question asks how many of the members' hometowns are from Maryland state, with a 30.4% counting question type.
The study provides two metrics, execution accuracy (EX) and valid efficiency score (VES), to evaluate text-to-SQL parsers facing real-world scenarios with large database contents. The authors decouple the question and SQL annotation procedures to make the situation more realistic and serve as the support for the diverse patterns of SQLs contained within the dataset. They also provide a comprehensive distribution analysis of SQLs across four dimensions, including No. of Keywords and No. n-grams / SQL (n=3) Toks / SQL and No. JOINs / SQL. If the benchmark receives enough attention, they plan to open-source full pairwise SQLs, but at this time they only open-source the optimal SQLs as the ground truth. The BIRD dataset is a large-scale cross-domain benchmark for text-to-SQL parsing. It contains two macro-categories: Fundamental Type and Reasoning Type, each containing 4-5 micro-categories. BIRD includes real-world applications of text-to-SQL, making it an ideal testing base for bridging the gap between academic use and practical use. The databases included in BIRD are extensive, and it incorporates the usage of window functions, knowledge grounding, and schema linking. BIRD covers a variety of domains and provides a more comprehensive difficulty analysis on the three difficulty levels, namely simple, moderate, and challenging.
To ensure the highest quality of data, the evaluation process includes examining each data in two dimensions: SQL validness and text-knowledge-SQL alignment. SQL validness is to ensure that each SQL can be executed and can return a valid result from the database. Text-knowledge-SQL alignment is involved to ensure that each SQL can be generated with the given texts and knowledge evidence. The SQL annotation approach involves two independent SQL annotators who generate SQLs for the same question without discussion. The created SQLs are executed in databases, and the responses are gathered if they are matched. Otherwise, the SQLs are checked with experts until a consensus is reached. The study employs a double-blind technique to qualify annotators for SQL query annotation. The team of annotators undergoes rigorous testing and is composed of skilled data engineers and database students. The BIRD dataset is compared to other cross-domain text-to-SQL benchmarks. The dataset takes into consideration execution efficiency and knowledge reasoning from the model. The study classifies knowledge into four categories: numeric reasoning, domain-specific, synonym, and composition. External knowledge is also utilized in the study. Detailed descriptions of database values are provided to aid readers in comprehending the database's structures and contents. The databases used in the study are open-source with appropriate licenses and should be distributed under the CC BY-SA 4.0 license. The document discusses text-to-SQL parsing for large-scale databases. The Database Description File contains information on database table and column names, as well as value descriptions to help annotators comprehend the structure of databases. Native speakers of English with degrees beyond the bachelor's level are hired to generate natural language questions regarding the contents of databases. The databases cover a variety of domains and are sourced from multiple resources, including CTU Prague Relational Learning Repository and Kaggle. The dataset construction involves acquiring open tables, synthesizing and standardizing schemas, and generating database relational data. The BIRD provides three formats of knowledge as bank in new questions. It is important for models to determine whether or not to select knowledge by themselves when facing real-world scenarios. Text-to-SQL is the process of converting natural language questions into SQL queries to retrieve relevant data from a database. The task involves formulating annotations and benchmarking, which is currently challenging for machines. The Spider SOTA model, which depends solely on the database schema, achieves better performance than models that incorporate external knowledge. To address this issue, BIRD, a new text-to-SQL benchmark, has been proposed to evaluate the efficiency of generated SQLs in addition to standard execution accuracy. A new metric called Valid Efficiency Score (VES) is introduced to promote more efficient query methods within the context of massive and noisy database contents. The proposed workflow involves specialists assembling and producing databases and description files, experts teaching and evaluating crowdsourcing people, and SQL annotators producing SQL files equipped with databases. The article proposes a benchmark called BIRD for text-to-SQL parsing in large-scale databases. The benchmark includes various databases and requires external knowledge and reasoning for accurate results. Current state-of-the-art models still struggle with this task, despite advancements in large language models. The benchmark also considers execution efficiency and noisy data values. The article provides examples of challenges and demonstrates the importance of external knowledge in generating accurate SQL queries. The document discusses text-to-SQL parsing for large-scale databases, which has attracted significant research interest from both academia and industry. Recent advances in neural networks have made it possible to automatically extract desired information from ubiquitous relational databases using natural language. The BIRD benchmark leaderboard and source code are available for advancing real-world applications of text-to-SQL research. The paper also provides an efficiency analysis to offer insights into generating text-to-efficient SQLs that are beneficial to industries. The study focuses on text-to-SQL parsing for large-scale databases and highlights the challenges that exist in achieving high execution accuracy. Even the most effective text-to-SQL models have only achieved a maximum of 92.96% execution accuracy, indicating the significance of database values in generating accurate text-to-SQLs. The study emphasizes the need for text-to-SQL models to feature database value comprehension in addition to semantic parsing. To address the challenges of dirty database contents and external knowledge between NL questions, the study presents BIRD, a big benchmark for large-scale databases grounded in text-to-SQL tasks. The prevalent benchmarks, such as Spider and WikiSQL, focus on database schema and executable SQLs, leaving a gap between academic study and real-world applications. The study highlights the importance of database values and presents BIRD as a solution to mitigate this gap.