AtScale, a leader in semantic layer technology, has launched an open, public leaderboard for Text-to-SQL (T2SQL) solutions, addressing a critical need for transparency and standardization in evaluating natural language data query capabilities. This resource enables academia, vendors, and developers to measure and compare T2SQL performance on a consistent, replicable benchmark using an industry standard open dataset, schema, and evaluation methods.
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"Enable Natural Language Prompting with AtScale's Semantic Layer & Generative AI"
The surge in interest for T2SQL solutions, fueled by Generative AI advancements, enables non-technical users to ask complex questions of proprietary data without SQL skills. However, inconsistent and proprietary evaluation methods make it challenging to validate or compare these solutions. AtScale's public benchmark solves this issue, providing an objective framework inspired by canonical benchmarks, like TPC-DS, and metrics that account for query and schema complexity.
"AtScale's leaderboard sets a new standard for transparency in Text-to-SQL evaluation," said John Langton, Head of Engineering at AtScale. "By creating an open, objective framework, we're enabling the industry to validate and improve solutions that make natural language data queries more accessible and reliable for everyone."
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The AtScale Text-to-SQL Leaderboard includes:
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