@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://aclanthology.org/2025.findings-acl.361/",
doi = "10.18653/v1/2025.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"
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<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</abstract>
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%0 Conference Proceedings
%T LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation
%A He, Xinrui
%A Ban, Yikun
%A Zou, Jiaru
%A Wei, Tianxin
%A Cook, Curtiss
%A He, Jingrui
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F he-etal-2025-llm
%X 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
%R 10.18653/v1/2025.findings-acl.361
%U https://aclanthology.org/2025.findings-acl.361/
%U https://doi.org/10.18653/v1/2025.findings-acl.361
%P 6921-6936
Markdown (Informal)
[LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation](https://aclanthology.org/2025.findings-acl.361/) (He et al., Findings 2025)
ACL