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Chat GPT bypass GPTZero detection

Hacker News

Chat GPT bypass GPTZero detection

I made a GPTs that bypass GPTZero, in my own limited tests, it has 100% bypass rate. The story: One of my best friend was accused by professor of using AI to write his essay, which is not true: he use translators for words he doesn't know. I know GPTZero is not accurate, I sent him a few articles and tell him to make appeals to the department. I'm really curious how GPTZero words under the hood. I open their website, paste 2 paragraphs, one generated by Bard, another I wrote myself. Of course, GPTZero knows mines was human and Bard is AI. I notice some wording differences between Bard, ChatGPT and me: AI has a better vocabulary than mine. I started to explore: replacing words. I replace words like "quest" to "looking for", "in addition" to "more", "earth" to "the planet" etc. and voila, it is now 0% AI generated. Surprising how easy it is to work around. Of course I didn't believe myself, hey, they are a company that has money from investors, are they lying? I checked their website and I saw this https://gptzero.me/writers , "so you can write words that AI will never generate". Huh? That's just so weird. It makes me feel that they know internally that GPTZero is not accurate, so they have to launch new products because of investors. According to the discovery I found about wording difference can affect GPTZero's detection, I quickly write a prompt for ChatGPT, in short: "Do not write paragraphs in parallel structure. Do not use SAT words, replace them with simple words or phrases". I did experiments for about 2 hours, and it actually bypassed GPTZero really stable!

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Actual performance

1points
Did not reach leaderboard

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86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, chatgpt, using · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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