arXiv (All)
open access
FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL Pairs
Relational databases are among the most widely deployed forms of structured knowledge, and natural language access to them requires grounding language onto schema entities and relations while handling the ambiguity inherent in how people phrase requests. Existing synthetic NL-to-SQL data generation
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL Pairs#1181634
- Assessing the Promise and Pitfalls of ChatGPT for Automated CS1-Driven Code Generation#1141217
- RDL-Bench: A Benchmark for Task-Aware Question Answering over Relational Databases#1356718
- Enabling Knowledge Graph Understanding at Scale with the EXplore Your Graphs ENgine (EXYGEN)#999090
- DualSQL: Text-to-SQL with Multi-Agent Reinforcement Learning#1011831
Record · ID 1188336
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive