LLM4FS: Leveraging Large Language Models for Feature Selection and How to Improve It

• URL: http://arxiv.org/abs/2503.24157v2
• Date: Mon, 14 Apr 2025 13:31:18 GMT
• Title: LLM4FS: Leveraging Large Language Models for Feature Selection and How to Improve It
• Authors: Jianhao Li, Xianchao Xiu,
• Abstract summary: We evaluate the state-of-the-art DeepSeek-R1, GPT-o3-mini, and GPT-4.5 feature selection methods.<n>We propose a novel hybrid strategy called LLM4FS that integrates LLMs with traditional data-driven methods.
• Score: 4.039099719779964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in large language models (LLMs) have provided new opportunities for decision-making, particularly in the task of automated feature selection. In this paper, we first comprehensively evaluate LLM-based feature selection methods, covering the state-of-the-art DeepSeek-R1, GPT-o3-mini, and GPT-4.5. Then, we propose a novel hybrid strategy called LLM4FS that integrates LLMs with traditional data-driven methods. Specifically, input data samples into LLMs, and directly call traditional data-driven techniques such as random forest and forward sequential selection. Notably, our analysis reveals that the hybrid strategy leverages the contextual understanding of LLMs and the high statistical reliability of traditional data-driven methods to achieve excellent feature selection performance, even surpassing LLMs and traditional data-driven methods. Finally, we point out the limitations of its application in decision-making.

Related papers

• Efficient Model Selection for Time Series Forecasting via LLMs [52.31535714387368]
  We propose to leverage Large Language Models (LLMs) as a lightweight alternative for model selection. Our method eliminates the need for explicit performance matrices by utilizing the inherent knowledge and reasoning capabilities of LLMs.
  arXiv  Detail & Related papers  (2025-04-02T20:33:27Z)
• LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers [10.282327560070202]
  Large Language Models (LLMs) have enabled the integration of domain knowledge into the feature engineering process. We propose LLM-FE, a novel framework that combines evolutionary search with the domain knowledge and reasoning capabilities of LLMs. Our results demonstrate that LLM-FE consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-03-18T17:11:24Z)
• 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.<n>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)
• LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
  We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression.<n>LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model.<n>Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
  arXiv  Detail & Related papers  (2025-02-15T02:55:22Z)
• Exploring Large Language Models for Feature Selection: A Data-centric Perspective [17.99621520553622]
  Large Language Models (LLMs) have influenced various domains, leveraging their exceptional few-shot and zero-shot learning capabilities. We aim to explore and understand the LLMs-based feature selection methods from a data-centric perspective. Our findings emphasize the effectiveness and robustness of text-based feature selection methods and showcase their potentials using a real-world medical application.
  arXiv  Detail & Related papers  (2024-08-21T22:35:19Z)
• LLM-Select: Feature Selection with Large Language Models [64.5099482021597]
  Large language models (LLMs) are capable of selecting the most predictive features, with performance rivaling the standard tools of data science. Our findings suggest that LLMs may be useful not only for selecting the best features for training but also for deciding which features to collect in the first place.
  arXiv  Detail & Related papers  (2024-07-02T22:23:40Z)
• One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models [67.49462724595445]
  Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs) We propose a novel method that involves learning scalable and pluggable virtual tokens for RAG.
  arXiv  Detail & Related papers  (2024-05-30T03:44:54Z)
• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models [52.98743860365194]
  We propose a new fine-tuning method called Self-Play fIne-tuNing (SPIN) At the heart of SPIN lies a self-play mechanism, where the LLM refines its capability by playing against instances of itself. This sheds light on the promise of self-play, enabling the achievement of human-level performance in LLMs without the need for expert opponents.
  arXiv  Detail & Related papers  (2024-01-02T18:53:13Z)
• Large Language Models as Data Preprocessors [9.99065004972981]
  Large Language Models (LLMs) have marked a significant advancement in artificial intelligence. This study explores their potential in data preprocessing, a critical stage in data mining and analytics applications. We propose an LLM-based framework for data preprocessing, which integrates cutting-edge prompt engineering techniques.
  arXiv  Detail & Related papers  (2023-08-30T23:28:43Z)
• On Learning to Summarize with Large Language Models as References [101.79795027550959]
  Large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarization datasets. We study an LLM-as-reference learning setting for smaller text summarization models to investigate whether their performance can be substantially improved.
  arXiv  Detail & Related papers  (2023-05-23T16:56:04Z)

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.