When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors
Evolving story · 1 updatesLLMs Struggle with Accurate Table Data ReferencingTimeline →Research introduces the first systematic evaluation of data referencing errors (DREs) in LLMs when processing tabular data, revealing widespread inaccuracies across models from 1.7B to 20B parameters.
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 across all tested models (1.7B to 20B parameter
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