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Bart Mofe Rl Xsum

Developed by praf-choub
MoFE is a model designed to control hallucination generation in abstractive summarization by mixing factual experts to reduce inaccuracies in summaries.
Downloads 23
Release Time : 5/3/2022

Model Overview

The MoFE model aims to reduce hallucination generation in abstractive summarization by mixing multiple factual experts, thereby improving the accuracy and reliability of summaries.

Model Features

Hallucination Reduction
Effectively reduces inaccuracies in summaries by mixing multiple factual experts.
High Accuracy
The model focuses on improving the accuracy and reliability of summaries.
Abstractive Summarization
Supports the generation of high-quality abstractive summaries.

Model Capabilities

Text Generation
Abstractive Summarization
Hallucination Reduction

Use Cases

Text Summarization
News Summarization
Generates abstractive summaries of news articles while reducing inaccuracies.
Improves the accuracy and reliability of summaries.
Academic Paper Summarization
Generates abstractive summaries of academic papers while ensuring information accuracy.
Reduces hallucination generation in summaries.
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