@inproceedings{deng-etal-2026-really,
    title = "What Really Matters for Table {LLM}s? A Meta-Evaluation of Model and Data Effects",
    author = "Deng, Naihao  and
      Zhang, Sheng  and
      Zhu, Henghui  and
      Chang, Shuaichen  and
      Zhang, Jiani  and
      Li, Alexander Hanbo  and
      Hang, Chung-Wei  and
      Kobayashi, Hideo  and
      Hu, Yiqun  and
      Ng, Patrick",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.195/",
    pages = "3755--3782",
    ISBN = "979-8-89176-386-9",
    abstract = "Table modeling has progressed for decades. In this work, we revisit this trajectory and highlight emerging challenges in the LLM era, particularly the paradox of choice: the difficulty of attributing performance gains amid diverse base models and training sets in the context of table instruction tuning. We replicate four table LLMs by instruction-tuning three foundation models on four existing datasets, yielding 12 models. We then evaluate these models across 16 table benchmarks. Our study is the first to quantitatively disentangle the effects of training data and base model selection, revealing that base model choice plays a more dominant role than the training data itself. Generalization and reasoning remain challenging, inviting future effort on table modeling. Based on our findings, we share our thoughts on the future directions for table modeling."
}