arXiv:2411.19504v2 Announce Type: replace Abstract: The advance of large language models (LLMs) has unlocked great opportunities in complex multi-modal data management tasks, particularly in question answering (QA) over complicated multi-table relational data. Despite significant progress, systematically evaluating LLMs on multi-table QA remains a critical challenge due to the inherent complexity of analyzing the modality of relational data structures and the potentially large scale of serialized tabular data. Existing benchmarks primarily focus on single-table QA, failing to capture the intri
The increased capabilities and deployment of large language models necessitates more rigorous and specific evaluation methods as their complexity grows, particularly in handling structured data.
Evaluating LLMs for multi-table question answering is crucial as enterprise data is largely relational, and enhanced capabilities in this area will significantly drive AI adoption in business intelligence and data management.
The explicit focus on multi-table QA evaluation, moving beyond single-table benchmarks, highlights a critical gap in current LLM assessment and development, indicating a shift towards more complex data interaction.
- · LLM developers focusing on structured data processing
- · Data-intensive enterprise sectors
- · AI-driven business intelligence platforms
- · LLMs with limited structured data understanding
- · Traditional, manual data analysis workflows
Improved benchmarks will lead to LLMs with superior multi-table question-answering capabilities.
Enterprises will increasingly leverage LLMs to automate complex data queries and derive insights from relational databases, reducing reliance on specialized data analysts for routine tasks.
The integration of LLMs with relational databases could lead to entirely new paradigms for data management and interaction, blurring the lines between natural language and structured query languages.
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