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Bart Mofe Rl Xsum
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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