When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors

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

Source: arXiv cs.CL — read the full report at the original publisher.

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