Summary of Lml-dap: Language Model Learning a Dataset For Data-augmented Prediction, by Praneeth Vadlapati
LML-DAP: Language Model Learning a Dataset for Data-Augmented Prediction
by Praneeth Vadlapati
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel approach to classification tasks using Large Language Models (LLMs) in an explainable manner. Unlike traditional Machine Learning (ML) models, which rely heavily on data cleaning and feature engineering, this method streamlines the process using LLMs. The proposed method, called “Language Model Learning (LML),” uses a new technique called “Data-Augmented Prediction (DAP)” to classify data. LML summarizes and evaluates datasets to determine the most relevant features for each label, while DAP generates queries to retrieve context-aware classification results. This approach unlocks new possibilities in areas requiring explainable and context-aware decisions, achieving high accuracy even with complex data. The system outperformed ML models in various scenarios, scoring above 90% in some test cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a better way to classify things using special language models called Large Language Models (LLMs). It’s different from the usual approach that uses Machine Learning (ML) models. The new method makes it easier by not needing as much data cleaning and feature engineering. Instead, it uses LLMs to look at the data and decide what features are most important for each category. This helps make decisions more understandable and context-aware. In tests, this system worked really well, even with complex data, and scored over 90% in some cases. |
Keywords
» Artificial intelligence » Classification » Feature engineering » Language model » Machine learning
