A new evaluation framework generates equivalent schemas from an E/R model and shows that LLMs produce SQL queries with different answers across those schemas for fixed questions and data.
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Chase-sql: Multi-path reasoning and preference optimized candidate selection in text-to-sql
16 Pith papers cite this work. Polarity classification is still indexing.
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Residual skill optimization creates complementary Text-to-SQL agents by training each new skill on prior ensemble failures, yielding accuracy gains on Spider2-Lite and transfer to other dialects and tasks.
CA-SQL achieves 51.72% execution accuracy on the challenging tier of the BIRD benchmark using GPT-4o-mini by scaling exploration breadth according to estimated task difficulty, evolutionary prompt seeding, and candidate voting.
DBCC applies SGCF-based offline compression and online purification to shrink database context by up to 100x while raising schema recall and execution accuracy 1.8-1.9% on Spider 2.0 and BIRD.
GATE bootstraps missing semantic groundings for text-to-SQL by testing open hypotheses through partial execution and storing the supported ones as reusable memory entries.
FINER-SQL boosts 3B-parameter small language models to 67.73% and 85% execution accuracy on BIRD and Spider benchmarks via dense memory and atomic rewards in group relative policy optimization, matching larger LLMs at lower latency.
TeCoD improves Text-to-SQL execution accuracy by up to 36% over in-context learning and cuts latency 2.2x on matched queries by extracting templates from historical pairs and enforcing them with constrained decoding.
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.
A multi-agent LLM framework with schema enrichment and business rules achieves 78.1% semantic accuracy on the BIRD NL2SQL benchmark.
APMPO boosts average Pass@1 scores on math reasoning benchmarks by 3 points over GRPO by using an adaptive power-mean policy objective and feedback-driven clipping bounds in RLVR training.
FREIA applies free energy principles and adaptive advantage shaping to unsupervised RL, outperforming baselines by 0.5-3.5 Pass@1 points on math reasoning with a 1.5B model.
MARS-SQL trains a multi-agent RL system with ReAct-style interaction and generative validation to produce SQL queries, reaching 77.84% execution accuracy on BIRD dev and 89.75% on Spider test.
XiYan-SQL achieves SOTA Text-to-SQL accuracy by combining schema filtering, a multi-generator ensemble fine-tuned on varied SQL formats, and a selection model.
CHESS deploys four LLM agents to retrieve information, prune schemas, generate refined SQL candidates, and validate via unit tests, reporting up to 71.10% accuracy on BIRD with 83% fewer calls than leading proprietary baselines.
EnoTab is a dual denoising framework for TableQA that performs evidence-based question denoising via semantic unit decomposition and evidence tree-guided table pruning with post-order rollback to improve performance on complex questions and large-scale tables.
citing papers explorer
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Same Data, Different Schemas: Robustness of LLM-based Text-to-SQL
A new evaluation framework generates equivalent schemas from an E/R model and shows that LLMs produce SQL queries with different answers across those schemas for fixed questions and data.
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Residual Skill Optimization for Text-to-SQL Ensembles
Residual skill optimization creates complementary Text-to-SQL agents by training each new skill on prior ensemble failures, yielding accuracy gains on Spider2-Lite and transfer to other dialects and tasks.
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CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation
CA-SQL achieves 51.72% execution accuracy on the challenging tier of the BIRD benchmark using GPT-4o-mini by scaling exploration breadth according to estimated task difficulty, evolutionary prompt seeding, and candidate voting.
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Database Context Compression for Text-to-SQL on Real-World Large Databases
DBCC applies SGCF-based offline compression and online purification to shrink database context by up to 100x while raising schema recall and execution accuracy 1.8-1.9% on Spider 2.0 and BIRD.
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Bootstrapping Semantic Layer from Execution for Text-to-SQL
GATE bootstraps missing semantic groundings for text-to-SQL by testing open hypotheses through partial execution and storing the supported ones as reusable memory entries.
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FINER-SQL: Boosting Small Language Models for Text-to-SQL
FINER-SQL boosts 3B-parameter small language models to 67.73% and 85% execution accuracy on BIRD and Spider benchmarks via dense memory and atomic rewards in group relative policy optimization, matching larger LLMs at lower latency.
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Reliable Answers for Recurring Questions: Boosting Text-to-SQL Accuracy with Template Constrained Decoding
TeCoD improves Text-to-SQL execution accuracy by up to 36% over in-context learning and cuts latency 2.2x on matched queries by extracting templates from historical pairs and enforcing them with constrained decoding.
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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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AgentNLQ: A General-Purpose Agent for Natural Language to SQL
A multi-agent LLM framework with schema enrichment and business rules achieves 78.1% semantic accuracy on the BIRD NL2SQL benchmark.
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Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning
APMPO boosts average Pass@1 scores on math reasoning benchmarks by 3 points over GRPO by using an adaptive power-mean policy objective and feedback-driven clipping bounds in RLVR training.
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Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs
FREIA applies free energy principles and adaptive advantage shaping to unsupervised RL, outperforming baselines by 0.5-3.5 Pass@1 points on math reasoning with a 1.5B model.
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MARS-SQL: A multi-agent reinforcement learning framework for Text-to-SQL
MARS-SQL trains a multi-agent RL system with ReAct-style interaction and generative validation to produce SQL queries, reaching 77.84% execution accuracy on BIRD dev and 89.75% on Spider test.
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XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL
XiYan-SQL achieves SOTA Text-to-SQL accuracy by combining schema filtering, a multi-generator ensemble fine-tuned on varied SQL formats, and a selection model.
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CHESS: Contextual Harnessing for Efficient SQL Synthesis
CHESS deploys four LLM agents to retrieve information, prune schemas, generate refined SQL candidates, and validate via unit tests, reporting up to 71.10% accuracy on BIRD with 83% fewer calls than leading proprietary baselines.
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When TableQA Meets Noise: A Dual Denoising Framework for Complex Questions and Large-scale Tables
EnoTab is a dual denoising framework for TableQA that performs evidence-based question denoising via semantic unit decomposition and evidence tree-guided table pruning with post-order rollback to improve performance on complex questions and large-scale tables.
- Data-aware candidate selection in NL2SQL translation via small separating instances