Due to the complexity of interpreting user questions, database schemas, and SQL production, accurately generating SQL from natural language queries (text-to-SQL) has been a long-standing difficulty. Traditional text-to-SQL systems using deep neural networks and human engineering have succeeded. Then, text-to-SQL jobs were tackled with pre-trained language models (PLMs), and they showed great promise.
Problems arise when PLMs with parameter constraints generate erroneous SQL due to the increasing complexity of both databases and the user questions that relate to them. This limits the use of PLM-based systems because it calls for optimization methods that are more complex and specialized. Recently, LLMs have proven to be quite adept in understanding natural language, especially when the model size is increased. Therefore, text-to-SQL research can benefit from the unique opportunities, enhancements, and solutions that can be brought about by integrating LLM-based implementation, such as improved query accuracy, better handling of complex queries, and increased system robustness.
There are three main areas into which the implementation details of LLM-based text-to-SQL fall:
- To begin with, the question comprehension is based on the NL question, which is a representation of the user’s purpose that the resulting SQL query is anticipated to match;
- Understanding the schema: The schema describes the database’s table and column structure and the text-to-SQL system needs to find the components that are relevant to the user’s query.
- The third step uses the parsing information to build SQL queries that may retrieve the desired answer by predicting the correct syntax. The LLMs have demonstrated the ability to execute a solid vanilla implementation thanks to the improved semantic parsing capabilities made possible by the larger training corpus.
The survey by Jinan University, Guangzhou, and the Hong Kong Polytechnic University is a comprehensive overview of the latest developments in LLM-based text-to-SQL, providing a thorough understanding of the best practices in the field.
Problems with Text-to-SQL
- Conversions Occurring from Ambiguity and Complex Structures: Because of the ambiguity and complexity of natural language questions, it takes a lot of knowledge and background information to correctly convert them into SQL queries.
- Database schemas can be complicated and differ substantially, making effective representation challenging; text-to-SQL solutions require an in-depth knowledge of these schemas.
- Some SQL queries contain complex or uncommon operations that are rarely seen in training data, making it difficult for models to produce these queries correctly.
- Due to differences in terminology, schema structure, and question patterns, models frequently fail to generalize across domains. However, with minimum domain-specific training, they can be effectively adapted.
Evolutionary Process
Since its inception, text-to-SQL has seen tremendous growth within the natural language processing (NLP) community, moving from rule-based to deep learning-based methodologies and, most recently, merging PLMs and LLMs.
- Methods Based on Rules: In the beginning, systems would employ heuristics and rules that were hand-crafted by humans to convert human-written text into SQL queries. The methods were good in small domains but needed to be more generalizable and flexible.
- Using long short-term memory (LSTM) and transformer deep neural networks, among others, enhanced the ability to generate SQL queries from plain English. Improving the ability to handle complicated queries and generalize across domains through techniques such as graph neural networks and intermediate representations.
- Methodology Based on Pre-Trained Language Models (PLMs): Text-to-SQL jobs were optimized using the semantic knowledge of pre-trained language models (PLMs) such as BERT and RoBERTa. To provide more precise SQL queries, schema-aware PLMs integrated knowledge of database structures.
- In SQL generation, large language models (LLMs), such as the GPT series, have demonstrated potential with the help of timely engineering and fine-tuning. This new field of study aims to enhance text-to-SQL efficiency and generalizability by taking advantage of LLMs’ knowledge and reasoning abilities.
Evaluation and Benchmarks in Text-to-SQL
- Dataset Categorization: The original release date of a dataset determines whether it is considered an “Original Dataset” or a “Post-annotated Dataset,” depending on whether it was modified from another dataset or not. Examining the original datasets for tables, rows, databases, and examples is the analysis process. Source and special settings are used to identify post-annotated datasets.
Both the original and post-annotated datasets use cross-domain data to mimic real-world applications.
- Knowledge-augmented datasets: BIRD and Spider-DK are examples of databases that leverage human-annotated external knowledge to enhance SQL generation by incorporating domain-specific information.
- Databases that are dependent on context: SParC and CoSQL are conversational SQL generators that generate several sub-question-SQL pairings to mimic conversations.
- Databases for Robustness: Spider-Realistic and ADVETA are two robustness datasets that assess system robustness by testing accuracy with disrupted database contents.
- CSpider (Chinese) and DuSQL (Chinese and English) are two cross-lingual datasets that can help with problems in non-English applications.
2. Quality Measures for Text-to-SQL: Metrics Based on Content Matching: These metrics use structural and syntactic similarities to compare the predicted SQL query to the ground truth. By comparing the F1 score, component matching (CM) determines how well expected and ground truth SQL components (such as SELECT and WHERE) match. A measure of how closely projected SQL queries match the ground truth in all components is known as exact matching (EM).
3. Metrics Based on Execution: These metrics compare the results obtained from running the SQL query on the target database with the predicted results to determine whether the generated query is correct.
Check out the Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter.
Join our Telegram Channel and LinkedIn Group.
If you like our work, you will love our newsletter..
Don’t Forget to join our 46k+ ML SubReddit
The post This Survey Paper Presents a Comprehensive Review of LLM-based Text-to-SQL appeared first on MarkTechPost.
#AIPaperSummary #AIShorts #Applications #ArtificialIntelligence #EditorsPick #LanguageModel #LargeLanguageModel #Staff #TechNews #Technology [Source: AI Techpark]