A team of researchers at the MIT Computer Science & Artificial Intelligence Lab (CSAIL) recently released a framework called TextFooler which successfully tricked state-of-the-art NLP models (such as ...
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Researchers at MIT have created a framework—TextFooler—that brought down the prediction accuracy of certain NLP models from 90% down to under 20% by simply using synonyms in place of certain words.
The news: Software called TextFooler can trick natural-language processing (NLP) systems into misunderstanding text just by replacing certain words in a sentence with synonyms. In tests, it was able ...
There are plenty of examples of AI models being fooled out there. From Google’s AI to detect images mistaking a turtle for a gun to Jigsaw’s AI to score toxic comments tricked to think a sentence is ...
Head over to our on-demand library to view sessions from VB Transform 2023. Register Here A recent paper coauthored by MIT researchers highlights the problem of sentence-level attacks against text ...
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