Evaluating LLMs in Database Scenarios: A Lifecycle Benchmark for Assessing Their Potential in Core Database Tasks
Shunfan Zheng, Dongsheng Shi, Yue Li, Xin Yi, Linlin Wang, Gerard de Melo
Why It Matters
What makes this one worth your time
This research is crucial for advancing the capabilities of LLMs in real-world database scenarios, moving beyond simplistic query generation to encompass full lifecycle management.
DBLifeBench offers a comprehensive evaluation framework for LLMs in database management.
Summary
The paper introduces DBLifeBench, a benchmark designed to evaluate Large Language Models (LLMs) across five phases of the database lifecycle, addressing the limitations of current evaluations that focus primarily on Text-to-SQL tasks.
Key contributions
- Development of DBLifeBench, a benchmark for evaluating LLMs across the database lifecycle.
- Proposal of Progressive-Text2SQL for improved interaction between natural language and SQL.
- Identification of performance discrepancies between general-purpose and specialized LLMs in various database tasks.
Notable insights
- The introduction of Progressive-Text2SQL aims to enhance LLMs' ability to handle complex SQL logic through structured reasoning, mimicking human problem-solving.
- The observation of 'catastrophic forgetting' in specialized models during non-coding phases highlights a significant challenge in LLM adaptability.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2608.03794v1 Announce Type: cross Abstract: Large Language Models (LLMs) are transforming database interaction paradigms, evolving from simple query translators to autonomous database administrators (DBAs). However, current evaluation benchmarks remain disproportionately fixated on Text-to-SQL tasks, neglecting the holistic Database Lifecycle-from initial schema design to post-deployment maintenance. This narrow focus fails to capture the diverse capabilities required for real-world database management. To bridge this gap, we introduce DBLifeBench, the first benchmark to evaluate LLMs across five critical lifecycle phases: Design, Implementation, Operation, Debugging, and Maintenance. Furthermore, addressing the cognitive mismatch between ambiguous natural language and complex SQL logic, we propose Progressive-Text2SQL, a novel task utilizing structured reasoning graphs to mimic human iterative problem-solving. Our extensive evaluation reveals a critical insight: while general-purpose models demonstrate balanced performance, specialized Text-to-SQL models suffer from ``catastrophic forgetting'' in non-coding phases like design and maintenance. DBLifeBench serves as a foundational step toward evaluating and building true full-stack database intelligence.