LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation

• URL: http://arxiv.org/abs/2410.21520v3
• Date: Sun, 05 Jan 2025 00:33:08 GMT
• Title: LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation
• Authors: Xinrui He, Yikun Ban, Jiaru Zou, Tianxin Wei, Curtiss B. Cook, Jingrui He,
• Abstract summary: Large language models (LLMs), trained on vast corpora, have shown strong potential in data generation. We propose a novel framework, LLM-Forest, which introduces a "forest" of few-shot learning LLM "trees" with confidence-based weighted voting. This framework is established on a new concept of bipartite information graphs to identify high-quality relevant neighboring entries.
• Score: 37.14344322899091
• License:
• 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 the risk of LLM hallucinations. To address these issues, we propose a novel framework, LLM-Forest, which introduces a "forest" of few-shot learning LLM "trees" with confidence-based weighted voting, inspired by 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.

Related papers

• From Selection to Generation: A Survey of LLM-based Active Learning [153.8110509961261]
  Large Language Models (LLMs) have been employed for generating entirely new data instances and providing more cost-effective annotations. This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques.
  arXiv  Detail & Related papers  (2025-02-17T12:58:17Z)
• Clear Minds Think Alike: What Makes LLM Fine-tuning Robust? A Study of Token Perplexity [61.48338027901318]
  We show that fine-tuning with LLM-generated data improves target task performance and reduces out-of-domain degradation. This is the first mechanistic explanation for the superior OOD robustness conferred by LLM-generated training data.
  arXiv  Detail & Related papers  (2025-01-24T08:18:56Z)
• Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach [56.55633052479446]
  Web-scale visual entity recognition presents significant challenges due to the lack of clean, large-scale training data. We propose a novel methodology to curate such a dataset, leveraging a multimodal large language model (LLM) for label verification, metadata generation, and rationale explanation. Experiments demonstrate that models trained on this automatically curated data achieve state-of-the-art performance on web-scale visual entity recognition tasks.
  arXiv  Detail & Related papers  (2024-10-31T06:55:24Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• PISTOL: Dataset Compilation Pipeline for Structural Unlearning of LLMs [31.16117964915814]
  Machine unlearning, which seeks to erase specific data stored in the pre-trained or fine-tuned models, has emerged as a crucial protective measure for LLMs. To facilitate the development of structural unlearning methods, we propose PISTOL, a pipeline for compiling multi-scenario datasets. We conduct benchmarks with four distinct unlearning methods on both Llama2-7B and Mistral-7B models.
  arXiv  Detail & Related papers  (2024-06-24T17:22:36Z)
• Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation [128.01050030936028]
  We propose an information refinement training method named InFO-RAG. InFO-RAG is low-cost and general across various tasks. It improves the performance of LLaMA2 by an average of 9.39% relative points.
  arXiv  Detail & Related papers  (2024-02-28T08:24:38Z)
• Learning to Reduce: Optimal Representations of Structured Data in Prompting Large Language Models [42.16047343029512]
  Large Language Models (LLMs) have been widely used as general-purpose AI agents. We propose a framework, Learning to Reduce, that fine-tunes a language model to generate a reduced version of an input context. We show that our model achieves comparable accuracies in selecting the relevant evidence from an input context.
  arXiv  Detail & Related papers  (2024-02-22T00:41:23Z)
• TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models [52.734140807634624]
  Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. Existing continual learning benchmarks lack sufficient challenge for leading aligned LLMs. We introduce TRACE, a novel benchmark designed to evaluate continual learning in LLMs.
  arXiv  Detail & Related papers  (2023-10-10T16:38:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.