@inproceedings{he-etal-2025-llm,
    title = "{LLM}-Forest: Ensemble Learning of {LLM}s with Graph-Augmented Prompts for Data Imputation",
    author = "He, Xinrui  and
      Ban, Yikun  and
      Zou, Jiaru  and
      Wei, Tianxin  and
      Cook, Curtiss  and
      He, Jingrui",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.361/",
    pages = "6921--6936",
    ISBN = "979-8-89176-256-5",
    abstract = "Missing data imputation is a critical challenge in various domains, such as healthcare and finance, where data completeness is vital for accurate analysis. Large language models (LLMs), trained on vast corpora, have shown strong potential in data generation, making them a promising tool for data imputation. However, challenges persist in designing effective prompts for a finetuning-free process and in mitigating biases and uncertainty in LLM outputs. To address these issues, we propose a novel framework, LLM-Forest, which introduces a ``forest'' of few-shot learning LLM ``trees'' with their outputs aggregated via confidence-based weighted voting based on LLM self-assessment, inspired by the ensemble learning (Random Forest). This framework is established on a new concept of bipartite information graphs to identify high-quality relevant neighboring entries with both feature and value granularity. Extensive experiments on 9 real-world datasets demonstrate the effectiveness and efficiency of LLM-Forest. The implementation is available at https://github.com/Xinrui17/LLM-Forest"
}