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New BIRD-History Benchmark Evaluates Text-to-SQL Systems Using Query Logs

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced BIRD-History, a new benchmark designed to evaluate text-to-SQL systems' ability to leverage historical query logs for understanding underspecified natural language questions. The benchmark includes 1,393 tasks across 11 databases, with annotations to identify relevant knowledge in past SQL scripts. A proposed plug-in retriever extracts and reranks external knowledge from these historical logs, demonstrating consistent performance improvements when integrated with existing text-to-SQL systems. AI

IMPACT This benchmark could improve the accuracy of text-to-SQL systems by enabling them to better understand context from historical data.

RANK_REASON The item describes a new benchmark and associated methodology for evaluating AI systems, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New BIRD-History Benchmark Evaluates Text-to-SQL Systems Using Query Logs

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The item describes a new benchmark and associated methodology for evaluating AI systems, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yunfan Zhou, Qiming Shi, Yizhou Yang, Di Weng, Yingcai Wu ·

    BIRD-History: A Benchmark for History-Driven Text-to-SQL with Fine-Grained Knowledge Annotations

    arXiv:2608.29345v1 Announce Type: new Abstract: While recent Large Language Model (LLM)-based text-to-SQL systems achieve impressive performance on standard benchmarks, they struggle when user queries implicitly rely on domain-specific knowledge, such as business logic, data conv…

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