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

• URL: http://arxiv.org/abs/2409.18957v2
• Date: Thu, 3 Oct 2024 17:57:07 GMT
• Title: LML-DAP: Language Model Learning a Dataset for Data-Augmented Prediction
• Authors: Praneeth Vadlapati,
• Abstract summary: This paper introduces a new approach to using Large Language Models (LLMs) for classification tasks in an explainable way. The proposed method uses the words "Act as an Explainable Machine Learning Model" in the prompt to enhance the interpretability of the predictions. In some test cases, the system scored an accuracy above 90%, proving the effectiveness of the system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Classification tasks are typically handled using Machine Learning (ML) models, which lack a balance between accuracy and interpretability. This paper introduces a new approach to using Large Language Models (LLMs) for classification tasks in an explainable way. 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. In the LML process, a dataset 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 and a row of the testing dataset to automatically generate 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 using context-aware decision-making. LML and DAP unlock the possibilities of new applications. 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

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