ROSE is an intent-centered NL2SQL metric using an adversarial Prover-Refuter cascade that achieves higher human-expert agreement than prior metrics on a new validation set.
C3: Zero -shot text-to-SQL with ChatGPT
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 7representative citing papers
NL2SQLBench is a new modular benchmarking framework that evaluates LLM NL2SQL methods across three core modules on existing datasets, exposing large accuracy gaps and computational inefficiency.
EGRefine optimizes column renamings via execution-grounded verification and view materialization to recover Text-to-SQL accuracy lost to schema naming issues while guaranteeing query equivalence.
PiLLar is the first LLM-guided Monte-Carlo Tree Search framework for joint schema-value matching on pivot tables, achieving 87.94% average accuracy on a new benchmark PTbench derived from real-world domains.
A self-healing LLM pipeline for natural language to PostgreSQL translation achieves up to 9.3 percentage point accuracy gains on benchmarks through error diagnosis and anti-regression mechanisms.
AV-SQL uses a pipeline of LLM agents to generate intermediate CTE views that decompose complex Text-to-SQL queries, reaching 70.38% execution accuracy on Spider 2.0.
SecureMCP integrates RBAC with five sequential defense modules in an MCP server to achieve 82.3% policy compliance against adversarial LLM SQL queries in AIoT while preserving execution accuracy.
citing papers explorer
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ROSE: An Intent-Centered Evaluation Metric for NL2SQL
ROSE is an intent-centered NL2SQL metric using an adversarial Prover-Refuter cascade that achieves higher human-expert agreement than prior metrics on a new validation set.
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NL2SQLBench: A Modular Benchmarking Framework for LLM-Enabled NL2SQL Solutions
NL2SQLBench is a new modular benchmarking framework that evaluates LLM NL2SQL methods across three core modules on existing datasets, exposing large accuracy gaps and computational inefficiency.
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EGREFINE: An Execution-Grounded Optimization Framework for Text-to-SQL Schema Refinement
EGRefine optimizes column renamings via execution-grounded verification and view materialization to recover Text-to-SQL accuracy lost to schema naming issues while guaranteeing query equivalence.
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PiLLar: Matching for Pivot Table Schema via LLM-guided Monte-Carlo Tree Search
PiLLar is the first LLM-guided Monte-Carlo Tree Search framework for joint schema-value matching on pivot tables, achieving 87.94% average accuracy on a new benchmark PTbench derived from real-world domains.
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SQL Query Engine: A Self-Healing LLM Pipeline for Natural Language to PostgreSQL Translation
A self-healing LLM pipeline for natural language to PostgreSQL translation achieves up to 9.3 percentage point accuracy gains on benchmarks through error diagnosis and anti-regression mechanisms.
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AV-SQL: Decomposing Complex Text-to-SQL Queries with Agentic Views
AV-SQL uses a pipeline of LLM agents to generate intermediate CTE views that decompose complex Text-to-SQL queries, reaching 70.38% execution accuracy on Spider 2.0.
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SecureMCP: A Policy-Enforced LLM Data Access Framework for AIoT Systems via Model Context Protocol
SecureMCP integrates RBAC with five sequential defense modules in an MCP server to achieve 82.3% policy compliance against adversarial LLM SQL queries in AIoT while preserving execution accuracy.