Summary of Veritas-nli : Validation and Extraction Of Reliable Information Through Automated Scraping and Natural Language Inference, by Arjun Shah et al.
VERITAS-NLI : Validation and Extraction of Reliable Information Through Automated Scraping and Natural Language Inference
by Arjun Shah, Hetansh Shah, Vedica Bafna, Charmi Khandor, Sindhu Nair
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel solution is proposed to address the challenges in fake news detection by leveraging web-scraping techniques and Natural Language Inference (NLI) models to retrieve external knowledge necessary for verifying the accuracy of a headline. The approach surpasses classical Machine Learning and Transformer-based models, achieving an accuracy of 84.3%. This highlights the efficacy of combining dynamic web-scraping with NLI to find support for a claimed headline in the corresponding externally retrieved knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fake news is spreading rapidly online, threatening public discourse and trust. Researchers have been studying machine learning and Transformer-based models to detect fake news, but these models are limited by their reliance on training data. A new approach uses web-scraping techniques and Natural Language Inference (NLI) to retrieve external knowledge needed to verify headlines. This solution is evaluated on a diverse dataset and outperforms previous methods. |
Keywords
» Artificial intelligence » Discourse » Inference » Machine learning » Transformer
