When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors
Summary
This paper systematically evaluates data referencing errors (DREs) in LLMs processing tables and proposes critic-based filtering and rejection sampling to improve answer accuracy. A lightweight 4B-parameter critic model achieves high detection accuracy for DREs.
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Paper page - When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors
Source: https://huggingface.co/papers/2606.32029
Abstract
Large language models exhibit data referencing errors when processing tables, which can be mitigated through critic-based filtering and rejection sampling, with a lightweight 4B-parameter model achieving high detection accuracy.
Whilelarge language models(LLMs) perform well ontable tasks, they still makedata 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 tabulardata referencing errorsacross different models and tasks. Our results show that DREs occur across all tested models (1.7B to 20B parameters). Furthermore, we demonstrate that incorporating data referencing as a critic significantly improvesanswer accuracyup to 12.0%, throughcritic-based filteringandrejection sampling. Finally, we trained a lightweight 4B-parameter critic model that achieves an averageF1 scoreof 78.2% in detecting bothin-distributionandout-of-distributionDREs, and effectively assists inference for larger models.
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