LML: Language Model Learning a Dataset for Data-Augmented Prediction
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
66faa0c2c2bf89d75e844ac5
Praneeth Vadlapati
claimed_verified
2024-09-30T15:51:27.140Z
66123b20ceb89d45d692fc5f
https://cdn-avatars.huggingface.co/v1/production/uploads/66123b20ceb89d45d692fc5f/89nz7lbvBOla-CzkHYWlp.png
Praneeth Vadlapati
user
prane-eth
LML: Language Model Learning a Dataset for Data-Augmented Prediction
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
A new approach using LLMs for classification tasks, called Language Model Learning (LML) with Data-Augmented Prediction (DAP), improves accuracy and interpretability without extensive data cleaning and feature engineering.
Language Model Learning (LML) Data-Augmented Prediction (DAP) Large Language Models (LLMs)