An AI detector mislabeled nearly every essay written by a non-native English speaker as being written by a bot
comprised of mounds of data scraped from all sorts of publicly available sources, like social media platforms and Wikipedia.
The researchers attributed the results of their study to a metric many GPT-detection systems use called"text perplexity." In an email to Insider, Stanford professor James Zou, who was the corresponding author of the study, said the metric"measures how surprising the word choices are in the text." The text that ChatGPT and other chatbots spit out is generally marked as low text perplexity by detectors because they're programmed to use the most common words or responses when constructing sentences. As a result, Zou said,"these detectors are more likely to flag text with low perplexity as AI-generated content."
When the detection systems evaluated the essays written by non-native English speakers, it marked much of their writing as low perplexity because they used"a more limited range of linguistic expression," the study said. This resulted in a vast majority of their content beingThe GPT detectors tested in the study aren't the only defensive tools producing paltry results.
. The newspaper fooled the sensors on several occasions, noting the detectors tested didn't use logical reasoning when assessing the authenticity of an image. in a blog post that the chatbot would"sometimes respond to harmful instructions or exhibit biased behavior." The company assured then that it was moving to"block certain types of unsafe content," but that it did expect the chatbot"to have some false negatives and positives" for the time being.
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