@inproceedings{wei-etal-2026-llms,
title = "Can {LLM}s Spot Data Anomalies That Make Table {QA} Impossible?",
author = "Wei, Yucheng and
Yang, Shenzhi and
Wang, Ning and
Zhang, Jinchang and
Yang, Yuhang and
Wang, Haobo and
Zha, Liangyu and
Ye, Wentao and
Li, Liyao and
Peng, Cheng and
Chen, Gang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1039/",
doi = "10.18653/2026.findings-emnlp.1039",
pages = "21952--21987",
ISBN = "979-8-89176-462-0",
abstract = "Existing tabular reasoning benchmarks typically assume that input tables are clean and error-free, which contrasts sharply with real-world data pipelines where noise and anomalies are pervasive. While humans routinely validate tables and reject conclusions when anomalies render a question unanswerable, it remains unclear whether large language models (LLMs) exhibit similar anomaly awareness. In this work, we introduce AnTBench, a benchmark designed to systematically evaluate LLM behavior under anomalous tabular data. AnTBench consists of 1,571 curated instances covering five common and verifiable anomaly types. Experiments on 14 representative LLMs reveal a pronounced capability{--}behavior gap: even the strongest model achieves 76.07{\%} on explicit anomaly detection, yet only 19.03{\%} on spontaneous anomaly perception during standard table QA. As an inference-time mitigation, we introduce the Multi-Path Thinker, a multi-path reasoning framework. While this approach improves explicit anomaly detection, enforcing proactive verification for every query is practically prohibitive. We urge the community to move beyond inference-time patches and instill spontaneous anomaly awareness directly into the training and alignment phases. Our code and data are available at https://github.com/Wei12097/AnTBench."
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<abstract>Existing tabular reasoning benchmarks typically assume that input tables are clean and error-free, which contrasts sharply with real-world data pipelines where noise and anomalies are pervasive. While humans routinely validate tables and reject conclusions when anomalies render a question unanswerable, it remains unclear whether large language models (LLMs) exhibit similar anomaly awareness. In this work, we introduce AnTBench, a benchmark designed to systematically evaluate LLM behavior under anomalous tabular data. AnTBench consists of 1,571 curated instances covering five common and verifiable anomaly types. Experiments on 14 representative LLMs reveal a pronounced capability–behavior gap: even the strongest model achieves 76.07% on explicit anomaly detection, yet only 19.03% on spontaneous anomaly perception during standard table QA. As an inference-time mitigation, we introduce the Multi-Path Thinker, a multi-path reasoning framework. While this approach improves explicit anomaly detection, enforcing proactive verification for every query is practically prohibitive. We urge the community to move beyond inference-time patches and instill spontaneous anomaly awareness directly into the training and alignment phases. Our code and data are available at https://github.com/Wei12097/AnTBench.</abstract>
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%0 Conference Proceedings
%T Can LLMs Spot Data Anomalies That Make Table QA Impossible?
%A Wei, Yucheng
%A Yang, Shenzhi
%A Wang, Ning
%A Zhang, Jinchang
%A Yang, Yuhang
%A Wang, Haobo
%A Zha, Liangyu
%A Ye, Wentao
%A Li, Liyao
%A Peng, Cheng
%A Chen, Gang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wei-etal-2026-llms
%X Existing tabular reasoning benchmarks typically assume that input tables are clean and error-free, which contrasts sharply with real-world data pipelines where noise and anomalies are pervasive. While humans routinely validate tables and reject conclusions when anomalies render a question unanswerable, it remains unclear whether large language models (LLMs) exhibit similar anomaly awareness. In this work, we introduce AnTBench, a benchmark designed to systematically evaluate LLM behavior under anomalous tabular data. AnTBench consists of 1,571 curated instances covering five common and verifiable anomaly types. Experiments on 14 representative LLMs reveal a pronounced capability–behavior gap: even the strongest model achieves 76.07% on explicit anomaly detection, yet only 19.03% on spontaneous anomaly perception during standard table QA. As an inference-time mitigation, we introduce the Multi-Path Thinker, a multi-path reasoning framework. While this approach improves explicit anomaly detection, enforcing proactive verification for every query is practically prohibitive. We urge the community to move beyond inference-time patches and instill spontaneous anomaly awareness directly into the training and alignment phases. Our code and data are available at https://github.com/Wei12097/AnTBench.
%R 10.18653/2026.findings-emnlp.1039
%U https://aclanthology.org/2026.findings-emnlp.1039/
%U https://doi.org/10.18653/2026.findings-emnlp.1039
%P 21952-21987
Markdown (Informal)
[Can LLMs Spot Data Anomalies That Make Table QA Impossible?](https://aclanthology.org/2026.findings-emnlp.1039/) (Wei et al., Findings 2026)
ACL
- Yucheng Wei, Shenzhi Yang, Ning Wang, Jinchang Zhang, Yuhang Yang, Haobo Wang, Liangyu Zha, Wentao Ye, Liyao Li, Cheng Peng, and Gang Chen. 2026. Can LLMs Spot Data Anomalies That Make Table QA Impossible?. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 21952–21987, Budapest, Hungary. Association for Computational Linguistics.