DSPy compiles short declarative programs into LM pipelines that self-optimize and outperform both standard few-shot prompting and expert-written chains on math, retrieval, and QA tasks.
Din-sql: Decomposed in-context learning of text-to-sql with self-correction
6 Pith papers cite this work. Polarity classification is still indexing.
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FlexSQL reaches 65.4% on Spider2-Snow by allowing agents to flexibly explore schemas, generate diverse plans, choose SQL or Python execution, and apply two-tiered repair.
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.
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.
A knowledge-aware Text-to-SQL framework constructs domain knowledge bases to generate synthetic data and enhance inference, claiming substantial gains on seven benchmarks especially in low-resource settings.
SSEV reaches 85.5-86.4% execution accuracy on Spider benchmarks and 66.3% on BIRD-Dev through self-refinement and voting; ReCAPAgent-SQL achieves 31% on initial Spider 2.0-Lite queries via agent collaboration.
citing papers explorer
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DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
DSPy compiles short declarative programs into LM pipelines that self-optimize and outperform both standard few-shot prompting and expert-written chains on math, retrieval, and QA tasks.
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FlexSQL: Flexible Exploration and Execution Make Better Text-to-SQL Agents
FlexSQL reaches 65.4% on Spider2-Snow by allowing agents to flexibly explore schemas, generate diverse plans, choose SQL or Python execution, and apply two-tiered repair.
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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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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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Knowledge Distillation for Low-Resource Open-source Text-to-SQL Model
A knowledge-aware Text-to-SQL framework constructs domain knowledge bases to generate synthetic data and enhance inference, claiming substantial gains on seven benchmarks especially in low-resource settings.
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LLM-Based SQL Generation: Prompting, Self-Refinement, and Adaptive Weighted Majority Voting
SSEV reaches 85.5-86.4% execution accuracy on Spider benchmarks and 66.3% on BIRD-Dev through self-refinement and voting; ReCAPAgent-SQL achieves 31% on initial Spider 2.0-Lite queries via agent collaboration.