ylai@lemmy.ml to Technology@lemmy.worldEnglish · 2 years agoVR Headsets Are Approaching the Eye’s Resolution Limitsspectrum.ieee.orgexternal-linkmessage-square32fedilinkarrow-up112arrow-down12
arrow-up110arrow-down1external-linkVR Headsets Are Approaching the Eye’s Resolution Limitsspectrum.ieee.orgylai@lemmy.ml to Technology@lemmy.worldEnglish · 2 years agomessage-square32fedilink
minus-squaredrislands@lemmy.worldlinkfedilinkEnglisharrow-up3arrow-down1·2 years ago…because they frequently do? Glaring errors are like, the main thing LLMs produce besides hype.
minus-squareKairuByte@lemmy.dbzer0.comlinkfedilinkEnglisharrow-up5arrow-down1·edit-22 years agoThey make glaring errors in logic, and confidently state things that are not true. But their whole “deal” is writing proper sentences based on predictive models. They don’t make mistakes like the excerpt highlighted.
minus-squareGlitterInfection@lemmy.worldlinkfedilinkEnglisharrow-up1·2 years agoPretty soon glaring errors like this will be the only way to identify human vs LLM writing. Then soon after that the LLMs will start producing glaring grammatical errors to match the humans.
minus-squaredrislands@lemmy.worldlinkfedilinkEnglisharrow-up1·2 years agoY’know what, that’s a fair point. Though I’m not the original commenter from the top, heh.
minus-squareKairuByte@lemmy.dbzer0.comlinkfedilinkEnglisharrow-up2·2 years agoAh apologies, I’m terrible with tracking usernames, I’ll edit for clarity.
minus-squaredrislands@lemmy.worldlinkfedilinkEnglisharrow-up1·2 years agoNo worries mate. I appreciate the correction regardless.
minus-squareGarbanzo@lemmy.worldlinkfedilinkEnglisharrow-up1·2 years agoI’m imagining that the first output didn’t cover everything they wanted so they tweaked it and pasted the results together and fucked it up.
…because they frequently do? Glaring errors are like, the main thing LLMs produce besides hype.
They make glaring errors in logic, and confidently state things that are not true. But their whole “deal” is writing proper sentences based on predictive models. They don’t make mistakes like the excerpt highlighted.
Pretty soon glaring errors like this will be the only way to identify human vs LLM writing.
Then soon after that the LLMs will start producing glaring grammatical errors to match the humans.
Y’know what, that’s a fair point. Though I’m not the original commenter from the top, heh.
Ah apologies, I’m terrible with tracking usernames, I’ll edit for clarity.
No worries mate. I appreciate the correction regardless.
I’m imagining that the first output didn’t cover everything they wanted so they tweaked it and pasted the results together and fucked it up.