title: LML: Language Model Learning a Dataset for Data-Augmented Prediction
summary: 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
paper_authors._id: 66faa0c2c2bf89d75e844ac5
paper_authors.name: Praneeth Vadlapati
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-30T15:51:27.140Z
paper_authors._id: 66123b20ceb89d45d692fc5f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66123b20ceb89d45d692fc5f/89nz7lbvBOla-CzkHYWlp.png
paper_authors.fullname: Praneeth
paper_authors.type: user
paper_authors.user: prane-eth
paper_title: LML: Language Model Learning a Dataset for Data-Augmented Prediction
paper_summary: 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
paper_ai_keywords: Large Language Models (LLMs) Language Model Learning (LML) Data-Augmented Prediction (DAP) Act as an Explainable Machine Learning Model