Evaluating LLMs in Database Scenarios: A Lifecycle Benchmark for Assessing Their Potential in Core Database Tasks

• URL: http://arxiv.org/abs/2608.03794v1
• Date: Tue, 04 Aug 2026 15:10:09 GMT
• Title: Evaluating LLMs in Database Scenarios: A Lifecycle Benchmark for Assessing Their Potential in Core Database Tasks
• Abstract summary: Large Language Models (LLMs) are transforming database interaction paradigms, evolving query translators to autonomous database administrators (DBAs)<n>We introduce DBLifeBench, the first benchmark to evaluate LLMs across five critical lifecycle phases: Design, Implementation, Operation, and Maintenance.
• Score: 29.600030491146175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Reflect-SQL: A Self-Reflection Based Framework for Text-to-SQL [3.052889399420755]
  Democratizing data access through natural language is a crucial goal for modern enterprises.<n>The practical adoption of Text-to-effective is critically hindered by real-world complexities.<n>We introduce Reflect-, a novel framework for Text tosql, grounded in multi-stage self-reflection approach.<n>Our framework achieves an execution accuracy of 72.03%, significantly outperforming state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-09-01T07:46:27Z)
• Agent-Agnostic Evaluation of SQL Accuracy in Production Text-to-SQL Systems [2.051598597332424]
  Text-to-native (T2native) evaluation in production environments poses fundamental challenges that existing benchmarks do not address.<n>We present STEF, a production- evaluation system that operates exclusively on natural language inputs.<n>STEF produces an interpretable 0 to 100 accuracy score via a composite metric that encompasses filter alignment, semantic verdict, and confidence of the evaluator.
  arXiv  Detail & Related papers  (2026-04-30T15:59:28Z)
• RASL: Retrieval Augmented Schema Linking for Massive Database Text-to-SQL [1.3654846342364308]
  We introduce a component-based retrieval architecture that decomposes database schemas and metadata into discrete semantic units.<n>Our solution enables practical text-to- interfaces across diverse enterprise settings without specialized fine-tuning.
  arXiv  Detail & Related papers  (2025-07-30T21:09:47Z)
• LLM-Symbolic Integration for Robust Temporal Tabular Reasoning [69.27153114778748]
  We introduce TempTabQA-C, a synthetic dataset designed for systematic and controlled evaluations.<n>This structured approach allows Large Language Models (LLMs) to generate and executesql queries, enhancing generalization and mitigating biases.
  arXiv  Detail & Related papers  (2025-06-06T05:14:04Z)
• IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis [60.32962597618861]
  IDA-Bench is a novel benchmark evaluating large language models in multi-round interactive scenarios.<n>Agent performance is judged by comparing its final numerical output to the human-derived baseline.<n>Even state-of-the-art coding agents (like Claude-3.7-thinking) succeed on 50% of the tasks, highlighting limitations not evident in single-turn tests.
  arXiv  Detail & Related papers  (2025-05-23T09:37:52Z)
• DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL [18.915121803834698]
  We propose DB-Explore, a novel framework that systematically aligns large language models with database knowledge.<n>Our framework enables comprehensive database understanding through diverse sampling strategies and automated instruction generation.
  arXiv  Detail & Related papers  (2025-03-06T20:46:43Z)
• Can Language Models Enable In-Context Database? [3.675766365690372]
  Large language models (LLMs) are emerging as few-shot learners capable of handling a variety of tasks. The lightweight and human readable characteristics of in-context database can potentially make it an alternative for the traditional database.
  arXiv  Detail & Related papers  (2024-11-04T05:25:39Z)
• Relational Database Augmented Large Language Model [59.38841050766026]
  Large language models (LLMs) excel in many natural language processing (NLP) tasks. They can only incorporate new knowledge through training or supervised fine-tuning processes. This precise, up-to-date, and private information is typically stored in relational databases.
  arXiv  Detail & Related papers  (2024-07-21T06:19:10Z)
• Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning [10.731045939849125]
  We focus on Text-to- semantic parsing from the perspective of retrieval-augmented generation. Motivated by challenges related to the size of commercial database schemata and the deployability of business intelligence solutions, we propose $textASTReS$ that dynamically retrieves input database information.
  arXiv  Detail & Related papers  (2024-07-03T15:55:14Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• On Evaluating the Integration of Reasoning and Action in LLM Agents with Database Question Answering [25.57202500348071]
  This study introduces a new long-form database question answering dataset designed to evaluate how Large Language Models interact with a database. The task requires LLMs to strategically generate multiplesql queries to retrieve sufficient data from a database, to reason with the acquired context, and to synthesize them into a comprehensive analytical narrative. We propose and evaluate two interaction strategies, and provide a fine-grained analysis of the individual stages within the interaction.
  arXiv  Detail & Related papers  (2023-11-16T09:55:07Z)
• Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation [76.76046657162306]
  Large language models (LLMs) have emerged as a new paradigm for Text-to- task. Large language models (LLMs) have emerged as a new paradigm for Text-to- task.
  arXiv  Detail & Related papers  (2023-08-29T14:59:54Z)
• SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended) [53.95151604061761]
  This paper introduces the framework for enhancing Text-to- filtering using large language models (LLMs) With few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error analyses. With instruction fine-tuning, we delve deep in understanding the critical paradigms that influence the performance of tuned LLMs.
  arXiv  Detail & Related papers  (2023-05-26T21:39:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.