Spotting AI Writing
Certainly, here are some examples of text generated by large language models. It's not just the use of em dashes, but the use of negative parallel structures like this. Wikipedia has compiled a list of signs that a piece of text has been generated by AI. We may soon get to a point where we won't be able to accurately distinguish human- and AI-generated content—at least, not without setting deliberate traps. Will it matter?
Key concepts
- The Detection Arms Race
- Every reliable tell becomes training data. Publish a list of habits that give a chatbot away and the next model is tuned not to have them, so a detector is always describing the generation it was built against. The list is a photograph of a moving target, which is why AI-text detectors stay unreliable.
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Every work — at a glance
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Large Language Models (technology)
Next-word predictors at enormous scale; the em dash and the tidy tricolon are habits, not fingerprints.
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What to know
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01
Detection by style is built to lose, and the working alternative is not detection at all. The tells are borrowed from human prose, so they are trained away model by model, and detectors misfire hardest on writers whose fluency is already unusual. What the hidden-instruction trap actually caught was not a machine's style but the absence of a reader — a student who cannot explain the argument they handed in.
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Across subjects
Theme connection
"Are We There Yet?"
Practice — a sample
SPCA published list of AI writing tells is MOST like —
- A A watermark
- B A fingerprint database
- C A locked door
- D A signed painting
- E A copied combination
Sign in to answer and see why each option is right or wrong.
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