@inproceedings{shaikh-2026-llms,
    title = "Can {LLM}s Self-Correct Table Reasoning Errors?",
    author = "Shaikh, Farseen",
    editor = "Gupta, Vivek  and
      Ding, Kaize  and
      Kokel, Harsha  and
      Zhao, Yue  and
      Agarwal, Amit  and
      Wang, Yu  and
      Glass, Michael  and
      Zhang, Yu  and
      Srinivas, Kavitha  and
      Chen, Xiusi  and
      Hassanzadeh, Oktie  and
      Zhu, Qi  and
      Chang, Shuaichen  and
      Luo, Yuan",
    booktitle = "Proceedings of the First Workshop on Structured Understanding, Retrieval, and Generation in the {LLM} Era ({SURG}e{LLM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.surgellm-1.19/",
    pages = "298--312",
    ISBN = "979-8-89176-406-4",
    abstract = "Self-correction{---}the ability of LLMs to detect and fix their own errors{---}has been studied extensively for mathematical and code reasoning, with limited prior work on table reasoning (primarily multi-agent pipelines such as Table-Critic, ACL 2025, rather than single-model structured prompting). Tables present unique challenges: errors arise from wrong cell retrieval, incorrect computation, flawed logic, and hallucination of values not present in the data. We conduct the first cross-provider single-model self-correction analysis for table reasoning across five providers (Google, Moonshot AI, Zhipu, Alibaba, MiniMax), testing five models (Gemini 3.1 Pro, Kimi K2.5, GLM 5, Qwen 3.5+, MiniMax M2.5) on WikiTableQuestions and TabFact with a multi-seed paired protocol. We propose Structured Self-Correction (SSC), a table-specific verification chain that guides models through cell verification, computation checking, logic validation, and completeness assessment. We confirm that the Accuracy-Correction Paradox (terminology from Li 2025) previously observed in math extends to tables: models with base accuracy in the mid-60s{--}mid-70s region benefit modestly from self-correction (multi-seed mean SCG up to +1.3{\%} with within-seed point estimates as high as +3.4{\%}), while stronger models above this region are systematically harmed by over-correction (multi-seed mean SCG down to -1.3{\%}, with 95{\%} bootstrap CIs significantly below zero). SSC reduces over-correction rates in 9 of 10 conditions, with reductions of 38{--}69{\%} on TabFact. An inference-mode-controlled probe shows that SSC{'}s qualitative direction is robust for Qwen 3.5+ across reasoning-ON and reasoning-OFF settings, while GLM 5 exhibits a substantial mode-dependent shift, indicating that mode robustness itself is model-dependent. Stronger baselines (self-consistency, self-critic, tool-augmented arithmetic verification, majority voting, and a same-family scaling probe) further characterize where SSC helps. Ablation studies reveal that answer-aware review is essential, reasoning traces aid error detection, and iterative correction shows diminishing returns. A FinQA domain transfer probe confirms a capability floor: self-correction fails when base task competence is very low (21.5{\%} accuracy). Our primary contribution is empirical: we characterize the conditions under which self-correction helps or harms table reasoning, providing actionable guidance for practitioners."
}