Summary of Llm-forest: Ensemble Learning Of Llms with Graph-augmented Prompts For Data Imputation, by Xinrui He et al.
LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation
by Xinrui He, Yikun Ban, Jiaru Zou, Tianxin Wei, Curtiss B. Cook, Jingrui He
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary LLMs, trained on vast corpora, have shown strong potential in data generation for missing data imputation, a critical challenge in domains like healthcare and finance. However, challenges persist in designing effective prompts for finetuning-free processes and mitigating the risk of LLM hallucinations. To address these issues, we propose LLM-Forest, a novel framework introducing 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. Our extensive experiments on 9 real-world datasets demonstrate the effectiveness and efficiency of LLM-Forest. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in data analysis called missing data imputation. It’s like trying to fill in the blanks in a puzzle. Large language models, which are really good at generating text, can be used to help with this task. But it’s not easy because we need to make sure the model doesn’t just make up random answers. To fix this problem, the authors created a new way of using these large language models called LLM-Forest. It works by combining many small models together and giving more weight to the ones that are most confident in their answers. The authors tested this method on 9 real-world datasets and it worked really well. |
Keywords
» Artificial intelligence » Few shot » Random forest
