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NL2SQL Translation Methods

Encoding Strategy

🎓Basic concept:

Encoding in the NL2SQL task refers to the process of transforming NL and database schema into a structured format that can be effectively utilized by a language model. This transformation is crucial as it converts unstructured and semi-structured data into a form that can be processed for generating SQL queries. The encoding process involves capturing the semantic meaning of the NL input and the structural information of the database schema, enabling the model to understand and map the user’s intent to the corresponding SQL query. There are three primary encoding strategies in NL2SQL models, each with its unique approach to transforming NL and database schemas: 1) sequential encoding, 2) graph-based encoding, and 3) separate encoding.

📚Representative papers：

  • Paper RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
  • Describe RESDSQL uses a ranking-enhanced encoder to sort and filter schema items, thereby reducing the complexity of schema linking during encoding. This method ensures that the most relevant schema items are prioritized, improving the overall efficiency of the encoding process.
  • Paper CatSQL: Towards Real World Natural Language to SQL Applications
  • Describe CatSQL utilizes the pre-trained GraPPa encoding network to concatenate the NL, database schema, and additional information into a single input sequence, generating hidden state sequences. This approach integrates multiple sources of information, enhancing the model’s ability to capture complex relationships.
  • Paper RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
  • Describe RAT-SQL introduces a relation-aware self-attention mechanism, allowing the model to explicitly consider and utilize predefined relational information when jointly encoding the question and the database schema. These relationships are represented as a graph structure, and through this graph-based encoding, RAT-SQL can more effectively capture the structural information in the schema and its alignment with the NL query.
  • Paper Towards Generalizable and Robust Text-to-SQL Parsing
  • Describe Based on the pre-trained T5 model, TKK employs task decomposition and multi-task learning strategies in encoding by breaking down the complex NL2SQL task into multiple subtasks and progressively acquiring and combining knowledge.

Decoding Strategy

🎓Basic concept:

Decoding plays a crucial role in NL2SQL translation, as it is responsible for converting the representations generated by the encoder into the target SQL queries. The choice of decoding strategy directly affects the quality and performance of the generated SQL queries. An excellent decoding strategy not only produces syntactically correct SQL queries but also ensures that the semantics of the SQL queries align with the NL and can even optimize the execution efficiency of the queries. We will introduce several key decoding strategies employed by existing NL2SQL models, namely: 1) greedy search-based decoding strategy, 2) beam search-based decoding strategy, and 3) constraint-aware incremental decoding strategy.

📚Representative papers：

  • Paper RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
  • Describe RAT-SQL combines relation-aware graph structure encoding and generation techniques. During the decoding process, RAT-SQL uses beam search to generate multiple candidate SQL queries, which are then reranked, and the optimal query is selected based on graph structure information.
  • Paper Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
  • Describe EditSQL employs a context encoding strategy, incorporating dialogue history information into the model. During the decoding process, it uses the beam search-based decoding strategy to generate candidate SQL queries and utilizes dialogue context information to select and optimize the queries.
  • Paper PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models
  • Describe The constraint-aware incremental decoding strategy, introduced by PICARD (Parsing Incrementally for Constrained Auto-Regressive Decoding), is specifically designed for NL2SQL tasks. This strategy aims to ensure the generation of syntactically correct SQL queries by incorporating constraints during the decoding process.
  • Paper Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing
  • Describe BRIDGE introduces some simple heuristic rules to prune the search space of the sequence decoder, proposing Schema-Consistency Guided Decoding to ensure that the generated SQL queries are consistent with the database schema. This strategy continuously checks whether the generated SQL queries match the database schema during the decoding process and adjusts the decoding path based on the matching results.

Task-specific Prompt Strategy

🎓Basic concept:

In the era of LLMs, prompt engineering can harness the capabilities of LLMs and has been widely adopted in natural language processing, with various frameworks developed for specific tasks. In the NL2SQL field, task-specific prompt strategy refers to the tailored prompt engineering techniques used in the NL2SQL translation process. These strategies instruct the LLMs to optimize the SQL query generation process according to task-specific rules, improving the accuracy of translating complex semantic NL query into the corresponding SQL query.

📚Representative papers：

  • Paper CHESS: Contextual Harnessing for Efficient SQL Synthesis
  • Describe CHESS transforms NL into SQL statements using a streamlined pipeline that relies on LLMs and CoT. This process comprises entity and context retrieval, schema selection, SQL generation, and revision.
  • Paper DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models
  • Describe DTS-SQL splits the work task into two subtasks, schema linking, and generation, to close the performance gap between open-source LLMs and closed-source LLMs.
  • Paper Towards Generalizable and Robust Text-to-SQL Parsing
  • Describe The TKK framework divides the initial NL2SQL parsing tasks into various small individual subtasks, with each corresponding to the mapping of the NL query to one or more clauses of the SQL query.
  • Paper MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL
  • Describe MAC-SQL incorporates a Decomposer agent designed to break down the user's original problem into several subproblems. This decomposition process aims to lessen the complexity of the origin question, enabling the generation of simpler SQL queries to solve each individual subproblem.
  • Paper DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction
  • Describe DIN-SQL employs a sophisticated categorization module for decomposition. It classify queries into distinct complexity groups: EASY, NON-NESTED, and NESTED, with the reference of NL and database schema.This module is fundamental for the subsequent decomposition process, which meticulously dissects complex queries into simpler sub-problems. By strategically identifying and separating schema linking, join conditions, and nested structures, the module facilitates a structured generation of SQL queries and amplifies the accuracy of translating complex the NL query into executable SQL.

Intermediate Representation for NL2SQL Translation

🎓Basic concept:

As mentioned before, the NL2SQL task is challenging due to the complexity and ambiguity of NL queries, as well as the formal and structured nature of SQL. Thus, researchers try to simplify this process by designing a grammar-free intermediate representation compared to SQL as the bridge between the ''free-form'' NL query and the ''constrained and formal'' SQL query.Roughly speaking, an intermediate representation (IR) is a structured yet flexible grammar that captures the essential components and relationships of an NL query without the strict syntax rules of SQL.

📚Representative papers：

  • Paper Schema-free SQL
  • Describe In the research of Schema-free SQL, the original question can be transformed into an intermediate representation even in the absence of user knowledge about schema information.
  • Paper SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task
  • Describe SyntaxSQLNet removes portions of the FROM and JOIN clauses in the syntax language.
  • Paper SemQL: a semantic query language for multidatabase systems
  • Describe SemQL removes the FROM, JOIN, ON and GROUP BY clauses and combines WHERE and HAVING conditions.
  • Paper Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
  • Describe EditSQL adds WHERE and HAVING conditions but retains the GROUP BY clause.
  • Paper Natural SQL: Making SQL Easier to Infer from Natural Language Specifications
  • Describe Natural SQL (NatSQL) is a widely recognized SQL-like syntax language that eliminates SQL statement operators, keywords, set operators, and other elements seldom found in user problem descriptions. It enhances schema linking by minimizing the necessary number of schema items.
  • Paper Semantic Decomposition of Question and SQL for Text-to-SQL Parsing
  • Describe The Query Plan Language (QPL) leverages the problem decomposition strategy to improve the parsing of intricate SQL queries. By breaking down a SQL query into modularized sub-queries, the complexity of the original query is reduced. This approach mitigates parsing difficulties associated with complex problems and cross-domain complex queries.
  • Paper Weakly Supervised Text-to-SQL Parsing through Question Decomposition
  • Describe Question Decomposition Meaning Representation (QDMR) decomposes the original question into a number of atomic questions. Each atomic question serves as an intermediate representation of the original question and can be translated into a set of small-scale formal operations involving tasks such as selecting entities, retrieving attributes, or aggregating information.
  • Paper Few-shot Text-to-SQL Translation using Structure and Content Prompt Learning
  • Describe The SC-prompt utilizes a two-stage divide and conquer method for NL2SQL parsing. During the initial phase, it instructs PLM to generate specific SQL structures, such as query commands and operators, while also supplying placeholders for any missing identifiers. In the subsequent phase, it directs the PLM to generate SQL structures containing actual values to fill the previously provided placeholders.
  • Paper CatSQL: Towards Real World Natural Language to SQL Applications
  • Describe CatSQL constructs a template sketch with slots serving as initial placeholders. Different from the former, this sketch is much more general. Its base model can focus on the parsing of user queries to fill these placeholders, consequently decreasing the computational resource cost. Furthermore, it implements a novel semantic correction algorithm to assess the semantic accuracy of the resulting SQL queries and rectify any semantic issues detected in the generated queries.
  • Paper Interleaving Pre-Trained Language Models and Large Language Models for Zero-Shot NL2SQL Generation
  • Describe ZeroNL2SQL integrates the schema alignment capabilities of PLM with the complex reasoning capabilities of LLMs. Initially, it utilizes PLM to produce SQL sketches for achieving schema alignment and subsequently employs LLMs to execute complex content reasoning for populating missing information. Additionally, it also proposes a predicate calibration method for guiding the design of language models for SQL sketches based on database instances and selecting the optimal SQL query.
  • Paper Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation
  • Describe TA-SQL combines pandas code and symbolic representation to generate an abstract sketch of SQL and uses this sketch to align with schema information in subsequent modules to generate complete SQL.
  • Paper RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
  • Describe RESDSQL introduces a rank-enhanced encoding and skeleton-aware decoding framework, which separates schema linking from skeleton parsing. During the decoding generation phase, its decoder initially produces the SQL skeleton and then generates the actual SQL query. This approach implicitly constrains the SQL parsing and governs the quality of generation. When combined with NatSQL, RESDSQL demonstrates the ability to further enhance the quality of SQL query generation.