arXiv:2606.32029v1 Announce Type: new Abstract: While large language models (LLMs) perform well on table tasks, they still make data referencing errors (DREs), i.e., incorrectly citing or omitting table values, despite understanding the table structure. Beyond final-answer accuracy, DREs directly compromise the correctness and reliability of intermediate reasoning steps. Yet prior studies have only offered limited, small-scale analyses. In this work, we present the first systematic evaluation of tabular data referencing errors across different models and tasks. Our results show that DREs occur
This research provides a systematic evaluation of LLM data referencing errors at a time when LLM deployment in enterprise workflows is accelerating, highlighting a critical limitation.
Organizations deploying LLMs for analytical tasks requiring high accuracy must understand and mitigate these 'data referencing errors' to ensure reliability and prevent flawed decision-making.
The focus for LLM integration in data-intensive applications will shift further towards robust error checking and potentially specialized architectures to address reliable data referencing, beyond just structural understanding.
- · AI guardrail developers
- · Data quality assurance platforms
- · Specialized LLM fine-tuning services
- · Generic LLM deployments in analytics
- · Organizations relying solely on LLM output without verification
- · LLM providers not prioritizing accuracy in tabular data handling
Increased demand for verification layers and tools to validate LLM outputs from tabular data.
A potential slowing of LLM adoption in highly regulated industries or critical analytical roles until these error modes are demonstrably reduced.
The development of new LLM architectures or pre-training methodologies specifically optimized for precise tabular data referencing, rather than just contextual understanding.
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Read at arXiv cs.CL