Abstract
This paper introduces a new approach to using Large Language Models (LLMs) for classification tasks, which are typically handled using Machine Learning (ML) models. Unlike ML models that rely heavily on data cleaning and feature engineering, this method streamlines the process using LLMs. This paper proposes a new concept called "Language Model Learning (LML)" powered by a new method called "Data-Augmented Prediction (DAP)". The classification is performed by LLMs using a method similar to humans manually exploring and understanding the data and deciding classifications using data as a reference. Training data is summarized and evaluated to determine the features that lead to the classification of each label the most. In the process of DAP, the system uses the data summary to automatically create a query, which is used to retrieve relevant rows from the dataset. A classification is generated by the LLM using data summary and relevant rows, ensuring satisfactory accuracy even with complex data. Usage of data summary and similar data in DAP ensures context-aware decision-making. The proposed method uses the words "Act as an Explainable Machine Learning Model" in the prompt to enhance the interpretability of the predictions by allowing users to review the logic behind each prediction. In some test cases, the system scored an accuracy above 90%, proving the effectiveness of the system and its potential to outperform conventional ML models in various scenarios. The code is available at this https URL
Abstract (translated)
本文提出了一种使用大型语言模型（LLMs）进行分类任务的新方法，通常使用机器学习（ML）模型处理。与依赖大量数据清洗和特征工程的ML模型不同，这种方法通过LLMs简化处理过程。本文提出了一种名为“语言模型学习（LML）”的新概念，由一种名为“数据增强预测（DAP）”的新方法驱动。分类是通过LLMs使用类似于人类手动探索和理解数据并使用数据作为参考的方法进行的。训练数据被总结和评估，以确定导致每个标签分类的最相关的特征。在DAP的过程中，系统使用数据概要自动创建查询，用于从数据集中检索相关行。LLM使用数据概要和相关行生成分类，确保在复杂数据情况下具有满意的准确度。使用数据概要和类似数据在DAP中确保了上下文感知决策。所提出的方法使用提示中的“作为可解释的机器学习模型”一词来增强预测的可解释性，使用户能够查看每个预测的逻辑。在某些测试用例中，系统的准确性超过90%，证明了系统的有效性和其在各种场景中超越传统ML模型的潜力。代码可在此处访问：https://www.example.com/
URL
https://arxiv.org/abs/2409.18957
