Improved NL2SQL based on Multi-layer Expert Network

• URL: http://arxiv.org/abs/2306.17727v3
• Date: Mon, 18 Sep 2023 03:39:23 GMT
• Title: Improved NL2SQL based on Multi-layer Expert Network
• Abstract summary: This study introduces a new approach called Multi-Layer Expert Generate SQL (MLEG-), which utilizes a dedicated multi-task hierarchical network. The lower layer of the network extracts semantic features of natural language statements, while the upper layer builds a specialized expert system for specific classification tasks. This hierarchical approach mitigates performance degradation resulting from different task conflicts.
• Score: 5.921227229250013
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Natural Language to SQL (NL2SQL) technique is used to convert natural language queries into executable SQL statements. Typically, slot-filling is employed as a classification method for multi-task cases to achieve this goal. However, slot-filling can result in inaccurate SQL statement generation due to negative migration issues arising from different classification tasks. To overcome this limitation, this study introduces a new approach called Multi-Layer Expert Generate SQL (MLEG-SQL), which utilizes a dedicated multi-task hierarchical network. The lower layer of the network extracts semantic features of natural language statements, while the upper layer builds a specialized expert system for handling specific classification tasks. This hierarchical approach mitigates performance degradation resulting from different task conflicts. The proposed method was evaluated on the WiKSQL dataset and was found to be effective in generating accurate SQL statements.

Related papers

• A Semantic-Layer-Mediated Agent for Natural Language to SQL over Heterogeneous Enterprise Databases [0.0]
  We present a semantic-layer-mediated NL2 agent that decouples semantic intent from physicalsql execution.<n>The system employs a constrained think-act loop, supports BigQuery, and Snowflake backends, and is integrated into an end-to-end evaluation framework.<n>Using Gemini 3 Pro, the system 94.15% execution accuracy on the 547-task Spider2-snow benchmark, ranking third on the official leaderboard and substantially outperforming schema-only approaches.
  arXiv  Detail & Related papers  (2026-06-30T02:22:39Z)
• Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL [50.84251174559123]
  Generative language models' performance does not extend equivalently to multi-turn Text-to-sourced.<n>We propose Track-, which enhances generative language models with dual-extractive modules designed to track schema and contextual changes in multi-turn Text-to-sourced.<n>Track- significantly improves execution accuracy in multi-turn interactions by 7.1% and 9.55% on datasets, respectively.
  arXiv  Detail & Related papers  (2026-03-06T07:57:06Z)
• CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description [15.080310729603466]
  CRED- is a framework designed for large-scale databases that integrates Cluster Retrieval and Execution Description.<n>It bridges the gap between natural language questions (NLQs) and their correspondingsql queries.<n>CRED- achieves new state-of-git-the-art (SOTA) performance, validating its effectiveness and scalability.
  arXiv  Detail & Related papers  (2025-08-18T09:43:07Z)
• CrackSQL: A Hybrid SQL Dialect Translation System Powered by Large Language Models [20.718779783349984]
  Crack is the first hybrid SQL dialect translation system that combines rule and LLM-based methods to overcome limitations. Crack supports three translation modes and offers multiple deployment options including a web console interface, a PyPI package, and a command-line prompt.
  arXiv  Detail & Related papers  (2025-04-01T15:11:03Z)
• Semantic Captioning: Benchmark Dataset and Graph-Aware Few-Shot In-Context Learning for SQL2Text [3.4688186440441893]
  Large Language Models (LLMs) have demonstrated remarkable performance in various NLP tasks. The reverse process, translating code into natural language, termed semantic captioning, has received less attention. In this paper, we focus on the captioning ofsql query (2Text) to address the critical need for understanding and explaining queries.
  arXiv  Detail & Related papers  (2025-01-06T17:36:09Z)
• An Actor-Critic Approach to Boosting Text-to-SQL Large Language Model [7.01795534825797]
  We propose a simple, general, and performance guaranteed T2S enhancement approach called Actor-Critic (AC) We design two roles using the same large language models (LLMs): an Actor to producesql queries and a Critic to evaluate the producedsql. If the Critic believes the producedsql is wrong, it notifies the Actor to reproduce thesql and perform evaluation again. We conducted extensive experiments on the Spider and related datasets with eleven LLMs, and demonstrated that the Actor-Critic method consistently improves the performance of T2S.
  arXiv  Detail & Related papers  (2024-10-28T15:22:35Z)
• Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection [23.423794784621368]
  Large Language Models (LLMs) face challenges due to schema issues and a lack of domain-specific database knowledge. This paper introduces a method of knowledge injection to enhance LLMs' ability to understand contents by incorporating prior knowledge.
  arXiv  Detail & Related papers  (2024-09-24T09:24:03Z)
• PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL [54.304872649870575]
  Large Language Models (LLMs) have emerged as powerful tools for Text-to-sense tasks. In this study, we propose that employing query group partitioning allows LLMs to focus on learning the thought processes specific to a single problem type.
  arXiv  Detail & Related papers  (2024-09-21T09:33:14Z)
• SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Merging [30.306023265985658]
  We introduce a framework for generating high-quality synthetic training data for any dialect. We propose a novel Mixture-of-Experts (MoE) that leverages the shared knowledge across dialects.
  arXiv  Detail & Related papers  (2024-08-22T20:50:48Z)
• Blar-SQL: Faster, Stronger, Smaller NL2SQL [0.0]
  We show how task decomposition can greatly benefit Large Language Models (LLMs) in database understanding and query generation. We propose a new framework to divide the schema into chunks in order to fit more information into a limited context. Our results are comparable with those obtained by GPT-4 at the same time being 135 times smaller, 90 times faster and more than 100 times cheaper than GPT-4.
  arXiv  Detail & Related papers  (2024-01-04T16:50:52Z)
• SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data [54.69489315952524]
  "Prompt" is designed to improve the few-shot prompting capabilities of Text-to-LLMs. "Prompt" outperforms previous approaches for in-context learning with few labeled data by a large margin. We show that emphPrompt outperforms previous approaches for in-context learning with few labeled data by a large margin.
  arXiv  Detail & Related papers  (2023-11-06T05:24:06Z)
• SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended) [53.95151604061761]
  This paper introduces the framework for enhancing Text-to- filtering using large language models (LLMs) With few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error analyses. With instruction fine-tuning, we delve deep in understanding the critical paradigms that influence the performance of tuned LLMs.
  arXiv  Detail & Related papers  (2023-05-26T21:39:05Z)
• Wav2SQL: Direct Generalizable Speech-To-SQL Parsing [55.10009651476589]
  Speech-to-Spider (S2Spider) aims to convert spoken questions intosql queries given databases. We propose the first direct speech-to-speaker parsing model Wav2 which avoids error compounding across cascaded systems. Experimental results demonstrate that Wav2 avoids error compounding and achieves state-of-the-art results by up to 2.5% accuracy improvement over the baseline.
  arXiv  Detail & Related papers  (2023-05-21T19:26:46Z)
• MIGA: A Unified Multi-task Generation Framework for Conversational Text-to-SQL [48.34333725045152]
  Most state-of-the-art conversational text-to-generative methods are incompatible with pre-trained language models (PLMs), such as T5. We present a two-stage unified MultI-task Generation frAmeme (MIGA) that leverages PLMs' ability to tackle conversational text-to-work.
  arXiv  Detail & Related papers  (2022-12-19T07:14:32Z)
• S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers [66.78665327694625]
  We propose S$2$, injecting Syntax to question- encoder graph for Text-to- relational parsing. We also employ the decoupling constraint to induce diverse edge embedding, which further improves the network's performance. Experiments on the Spider and robustness setting Spider-Syn demonstrate that the proposed approach outperforms all existing methods when pre-training models are used.
  arXiv  Detail & Related papers  (2022-03-14T09:49:15Z)
• Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsing [44.0348697408427]
  The task of multi-turn text-to- semantic parsing aims to translate natural language utterances in an interaction intosql queries. A graph relational network and a non-linear layer are designed to update the representations of these two states respectively. Experimental results on the challenging Co dataset demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2020-12-09T11:59:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.