EvoMQL uses iterative Draft-Refine-Optimize cycles with execution feedback to reach 76.6% accuracy on EAI and 83.1% on TEND benchmarks for natural language to MongoDB query generation.
arXiv preprint arXiv:1807.03100 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
We consider the problem of neural semantic parsing, which translates natural language questions into executable SQL queries. We introduce a new mechanism, execution guidance, to leverage the semantics of SQL. It detects and excludes faulty programs during the decoding procedure by conditioning on the execution of partially generated program. The mechanism can be used with any autoregressive generative model, which we demonstrate on four state-of-the-art recurrent or template-based semantic parsing models. We demonstrate that execution guidance universally improves model performance on various text-to-SQL datasets with different scales and query complexity: WikiSQL, ATIS, and GeoQuery. As a result, we achieve new state-of-the-art execution accuracy of 83.8% on WikiSQL.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Structural dependency graphs and staged pre-execution verification raise LLM-based EDA code pass rates to 82.5% (single-step) and 70-84% (multi-step) while halving tool calls by catching dependency violations before runtime.
citing papers explorer
-
Draft-Refine-Optimize: Self-Evolved Learning for Natural Language to MongoDB Query Generation
EvoMQL uses iterative Draft-Refine-Optimize cycles with execution feedback to reach 76.6% accuracy on EAI and 83.1% on TEND benchmarks for natural language to MongoDB query generation.
-
Structural Verification for Reliable EDA Code Generation without Tool-in-the-Loop Debugging
Structural dependency graphs and staged pre-execution verification raise LLM-based EDA code pass rates to 82.5% (single-step) and 70-84% (multi-step) while halving tool calls by catching dependency violations before runtime.