TQA-Bench: Evaluating LLMs for Multi-Table Question Answering

Researchers introduce TQA-Bench, a comprehensive benchmark for evaluating large language models on multi-table question answering tasks using real-world datasets with variable context lengths (8K-64K tokens). The evaluation of LLMs ranging from 2 billion to 671 billion parameters reveals significant performance gaps in handling complex relational data structures, addressing a critical gap in existing benchmarks that focus primarily on single-table QA.

TQA-Bench addresses a meaningful gap in AI evaluation methodology by focusing on multi-table question answering, a capability essential for real-world applications in finance, healthcare, and e-commerce. Traditional benchmarks have concentrated on simpler single-table QA tasks, failing to capture the complexity of joining and reasoning across multiple relational tables—a fundamental requirement in enterprise data environments. This research matters because it provides a standardized way to assess whether LLMs can handle the kind of multi-step reasoning and context management that practical database query tasks demand.

The benchmark's design is particularly notable for its flexible context length sampling mechanism (8K-64K tokens), allowing researchers to measure how model performance degrades as information density increases. This directly tests whether models genuinely understand relational structures or simply pattern-match within constrained contexts. By evaluating models across a 335x parameter range (2B to 671B), the research reveals scaling trends and identifies whether larger models genuinely solve multi-table reasoning or merely memorize patterns more effectively.

For the AI development community, TQA-Bench establishes clearer performance expectations for production use cases. Organizations considering LLM deployment for data analytics and business intelligence can benchmark their own implementations against this standard. The research highlights that multi-table QA remains challenging for current models, signaling opportunities for specialized fine-tuning approaches or architectural innovations designed specifically for relational reasoning.

• →TQA-Bench fills a critical evaluation gap by testing LLMs on realistic multi-table question answering rather than simplified single-table tasks.
• →Context length variations (8K-64K tokens) expose performance degradation patterns across model scales, revealing genuine reasoning limitations versus memorization.
• →Systematic evaluation across 2B-671B parameter models shows scaling trends for relational data handling, informing deployment decisions for enterprise applications.
• →The benchmark uses real-world public datasets with symbolic extensions to measure reasoning beyond simple retrieval and pattern matching capabilities.
• →Results indicate multi-table QA remains a significant challenge for current LLMs, highlighting development opportunities in relational reasoning architectures.