[2410.12164] Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Generator-Validator Fine-tuning

Computer Science > Computation and Language arXiv:2410.12164 (cs) [Submitted on 16 Oct 2024 (v1), last revised 23 Mar 2026 (this version, v2)] Title:Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Generator-Validator Fine-tuning Authors:Junjie Xing, Yeye He, Mengyu Zhou, Haoyu Dong, Shi Han, Dongmei Zhang, Surajit Chaudhuri View a PDF of the paper titled Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Generator-Validator Fine-tuning, by Junjie Xing and 6 other authors View PDF HTML (experimental) Abstract:Language models such as GPT and Llama have shown remarkable ability on diverse natural language tasks, yet their performance on complex table tasks (e.g., NL-to-Code and data cleaning) remains suboptimal. Improving performance typically requires task-specific fine-tuning, which depends on expensive human labeling and is prone to overfitting. In this work, we propose Table-LLM-Specialist, a self-trained fine-tuning paradigm designed for table tasks. Our key insight is that many table tasks admit two dual formulations: a generative version and a classification version. Leveraging this duality, we introduce a Generator-Validator paradigm that iteratively generates and validates training data using language models, enabling effective fine-tuning without manually labeled data. Extensive evaluations on Llama, GPT-3.5, and GPT-4 show that Table-LLM-Specialist achieves (1) strong performance across diverse tasks compared to ...