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Buffer overflow and SQL Injection Detection using LSTM-based models

open access: yes
Trabajo de Fin de Grado en Ingeniería Informática, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2023/2024A medida que la tecnología avanza y los sistemas se interconectan, el rol que desempeña la ...
Sun, Jing, Qiu Zhao, Yingting
core  

CodeS: Towards Building Open-source Language Models for Text-to-SQL [PDF]

open access: yesProc. ACM Manag. Data
Language models have shown promising performance on the task of translating natural language questions into SQL queries (Text-to-SQL). However, most of the state-of-the-art (SOTA) approaches rely on powerful yet closed-source large language models (LLMs),
Haoyang Li, Hanbing Liu, Ju Fan
exaly   +2 more sources
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OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale

Proceedings of the VLDB Endowment
Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases.
Haoyang Li   +11 more
semanticscholar   +1 more source

Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows

International Conference on Learning Representations
Real-world enterprise text-to-SQL workflows often involve complex cloud or local data across various database systems, multiple SQL queries in various dialects, and diverse operations from data transformation to analytics.
Fangyu Lei   +16 more
semanticscholar   +1 more source

CHESS: Contextual Harnessing for Efficient SQL Synthesis

arXiv.org
Translating natural language questions into SQL queries, known as text-to-SQL, is a long-standing research problem. Effective text-to-SQL synthesis can become very challenging due to (i) the extensive size of database catalogs (descriptions of tables and
Shayan Talaei   +4 more
semanticscholar   +1 more source

OpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-shot and Consistency Alignment

Proc. ACM Manag. Data
Although multi-agent collaborative Large Language Models (LLMs) have achieved significant breakthroughs in the Text-to-SQL task, their performance is still constrained by various factors. These factors include the incompleteness of the framework, failure
Xiangjin Xie   +3 more
semanticscholar   +1 more source

SQL-R1: Training Natural Language to SQL Reasoning Model By Reinforcement Learning

arXiv.org
Natural Language to SQL (NL2SQL) enables intuitive interactions with databases by transforming natural language queries into structured SQL statements.
Peixian Ma   +5 more
semanticscholar   +1 more source

Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL

arXiv.org
Text-to-SQL is a challenging task involving multiple reasoning-intensive subtasks, including natural language understanding, database schema comprehension, and precise SQL query formulation.
Mohammadreza Pourreza   +7 more
semanticscholar   +1 more source

CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL

International Conference on Learning Representations
In tackling the challenges of large language model (LLM) performance for Text-to-SQL tasks, we introduce CHASE-SQL, a new framework that employs innovative strategies, using test-time compute in multi-agent modeling to improve candidate generation and ...
Mohammadreza Pourreza   +9 more
semanticscholar   +1 more source

Alpha-SQL: Zero-Shot Text-to-SQL using Monte Carlo Tree Search

International Conference on Machine Learning
Text-to-SQL, which enables natural language interaction with databases, serves as a pivotal method across diverse industries. With new, more powerful large language models (LLMs) emerging every few months, fine-tuning has become incredibly costly, labor ...
Boyan Li   +6 more
semanticscholar   +1 more source

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