Related papers: Exploring Large Language Models for Feature Selection: A Data-centric Perspective

Exploring Large Language Models for Feature Selection: A Data-centric Perspective

URL: http://arxiv.org/abs/2408.12025v2
Date: Wed, 23 Oct 2024 17:01:05 GMT
Title: Exploring Large Language Models for Feature Selection: A Data-centric Perspective
Authors: Dawei Li, Zhen Tan, Huan Liu,
Abstract summary: 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.
Score: 17.99621520553622
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The rapid advancement of Large Language Models (LLMs) has significantly influenced various domains, leveraging their exceptional few-shot and zero-shot learning capabilities. In this work, we aim to explore and understand the LLMs-based feature selection methods from a data-centric perspective. We begin by categorizing existing feature selection methods with LLMs into two groups: data-driven feature selection which requires numerical values of samples to do statistical inference and text-based feature selection which utilizes prior knowledge of LLMs to do semantical associations using descriptive context. We conduct experiments in both classification and regression tasks with LLMs in various sizes (e.g., GPT-4, ChatGPT and LLaMA-2). Our findings emphasize the effectiveness and robustness of text-based feature selection methods and showcase their potentials using a real-world medical application. We also discuss the challenges and future opportunities in employing LLMs for feature selection, offering insights for further research and development in this emerging field.

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)
Evaluating Linguistic Capabilities of Multimodal LLMs in the Lens of Few-Shot Learning [15.919493497867567]
This study aims to evaluate the performance of Multimodal Large Language Models (MLLMs) on the VALSE benchmark. We conducted a comprehensive assessment of state-of-the-art MLLMs, varying in model size and pretraining datasets.
arXiv Detail & Related papers (2024-07-17T11:26:47Z)
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)
Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions [2.5179515260542544]
Large Language Models (LLMs) have gained significant attention across academia and industry for their versatile applications in text generation, question answering, and text summarization. To quantify the performance, it's crucial to have a comprehensive grasp of existing metrics. This paper offers a comprehensive exploration of LLM evaluation from a metrics perspective, providing insights into the selection and interpretation of metrics currently in use.
arXiv Detail & Related papers (2024-04-14T03:54:00Z)
Large Language Models for Data Annotation and Synthesis: A Survey [49.8318827245266]
This survey focuses on the utility of Large Language Models for data annotation and synthesis. It includes an in-depth taxonomy of data types that LLMs can annotate, a review of learning strategies for models utilizing LLM-generated annotations, and a detailed discussion of the primary challenges and limitations associated with using LLMs for data annotation and synthesis.
arXiv Detail & Related papers (2024-02-21T00:44:04Z)
Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
arXiv Detail & Related papers (2023-12-26T07:24:46Z)
A Survey on Large Language Models for Recommendation [77.91673633328148]
Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) This survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
arXiv Detail & Related papers (2023-05-31T13:51:26Z)
Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning [104.58874584354787]
In recent years, pre-trained large language models (LLMs) have demonstrated remarkable efficiency in achieving an inference-time few-shot learning capability known as in-context learning. This study aims to examine the in-context learning phenomenon through a Bayesian lens, viewing real-world LLMs as latent variable models.
arXiv Detail & Related papers (2023-01-27T18:59:01Z)

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