语言模型学习：用于数据增强预测的数据集学习

LML: Language Model Learning a Dataset for Data-Augmented Prediction

September 27, 2024

作者: Praneeth Vadlapati

cs.AI

摘要

本文介绍了一种新的方法，使用大型语言模型（LLMs）进行分类任务，这些任务通常使用机器学习（ML）模型处理。与依赖数据清洗和特征工程的ML模型不同，这种方法利用LLMs简化了流程。本文提出了一个名为“语言模型学习（LML）”的新概念，由一种名为“数据增强预测（DAP）”的新方法提供支持。分类由LLMs执行，使用一种类似于人类手动探索和理解数据并根据数据作为参考进行分类决策的方法。训练数据被总结和评估，以确定导致每个标签分类的特征。在DAP过程中，系统使用数据摘要自动创建一个查询，用于从数据集中检索相关行。LLMs使用数据摘要和相关行生成分类，确保即使在复杂数据情况下也能获得令人满意的准确性。在DAP中使用数据摘要和类似数据确保了上下文感知的决策制定。所提出的方法在提示中使用“作为可解释的机器学习模型”一词，以增强预测的可解释性，使用户能够审查每个预测背后的逻辑。在某些测试案例中，系统的准确率超过90％，证明了系统的有效性及其在各种情况下超越传统ML模型的潜力。代码可在https://github.com/Pro-GenAI/LML-DAP找到。

English

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 https://github.com/Pro-GenAI/LML-DAP