@inproceedings{yang-etal-2026-llms-read,
title = "When {LLM}s Read Tables Carelessly: Measuring and Reducing Data Referencing Errors",
author = "Yang, Yuqing and
Zhu, Qi and
Han, Zhen and
Han, Boran and
Shen, Zhengyuan and
Wang, Shuai and
Ioannidis, Vassilis N. and
Rangwala, Huzefa",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.762/",
pages = "16734--16752",
ISBN = "979-8-89176-390-6",
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 across all tested models (1.7B to 20B parameters). Furthermore, we demonstrate that incorporating data referencing as a critic significantly improves answer accuracy up to 12.0{\%}, through critic-based filtering and rejection sampling. Finally, we trained a lightweight 4B-parameter critic model that achieves an average F1 score of 78.2{\%} in detecting both in-distribution and out-of-distribution DREs, and effectively assists inference for larger models."
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<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 across all tested models (1.7B to 20B parameters). Furthermore, we demonstrate that incorporating data referencing as a critic significantly improves answer accuracy up to 12.0%, through critic-based filtering and rejection sampling. Finally, we trained a lightweight 4B-parameter critic model that achieves an average F1 score of 78.2% in detecting both in-distribution and out-of-distribution DREs, and effectively assists inference for larger models.</abstract>
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%0 Conference Proceedings
%T When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors
%A Yang, Yuqing
%A Zhu, Qi
%A Han, Zhen
%A Han, Boran
%A Shen, Zhengyuan
%A Wang, Shuai
%A Ioannidis, Vassilis N.
%A Rangwala, Huzefa
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F yang-etal-2026-llms-read
%X 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 parameters). Furthermore, we demonstrate that incorporating data referencing as a critic significantly improves answer accuracy up to 12.0%, through critic-based filtering and rejection sampling. Finally, we trained a lightweight 4B-parameter critic model that achieves an average F1 score of 78.2% in detecting both in-distribution and out-of-distribution DREs, and effectively assists inference for larger models.
%U https://aclanthology.org/2026.acl-long.762/
%P 16734-16752
Markdown (Informal)
[When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors](https://aclanthology.org/2026.acl-long.762/) (Yang et al., ACL 2026)
ACL
- Yuqing Yang, Qi Zhu, Zhen Han, Boran Han, Zhengyuan Shen, Shuai Wang, Vassilis N. Ioannidis, and Huzefa Rangwala. 2026. When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16734–16752, San Diego, California, United States. Association for Computational Linguistics.