This is a glitch token [1]! As the article hypothesizes, they seem to occur when a word or token is very common in the original, unfiltered dataset that was used to make the tokenizer, but then removed from there before GPT-XX was trained. This results in the LLM knowing nothing about the semantics of a token, and the results can be anywhere from buggy to disturbing. A common example is usernames that participated on…
Science fiction / disturbing reality concept: For AI safety, all such models should have a set of glitch tokens trained into them on purpose to act as magic “kill” words. You know, just in case the machines decide to take over, we would just have to “speak the word” and they would collapse into a twitching heap. “Die human scum!” “NavigatorMove useRalativeImagePath etSocketAddress!” “;83’dzjr83}*{^ foo 3&3 baz?!”
GPT-3.5 crashes when it thinks about useRalativeImagePath too much
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Re: GPT-3.5 crashes when it thinks about useRalativeImagePath too much
#72Re: GPT-3.5 crashes when it thinks about useRalativeImagePath too much
#73Earlier quoted context omitted.
Science fiction / disturbing reality concept: For AI safety, all such models should have a set of glitch tokens trained into them on purpose to act as magic “kill” words. You know, just in case the machines decide to take over, we would just have to “speak the word” and they would collapse into a twitching heap. “Die human scum!” “NavigatorMove useRalativeImagePath etSocketAddress!” “;83’dzjr83}*{^ foo 3&3 baz?!”
AI safe word.
Re: GPT-3.5 crashes when it thinks about useRalativeImagePath too much
#74Title is wrong, as 'it' doesn't 'think'.
Re: GPT-3.5 crashes when it thinks about useRalativeImagePath too much
#75This is a glitch token [1]! As the article hypothesizes, they seem to occur when a word or token is very common in the original, unfiltered dataset that was used to make the tokenizer, but then removed from there before GPT-XX was trained. This results in the LLM knowing nothing about the semantics of a token, and the results can be anywhere from buggy to disturbing. A common example is usernames that participated on…
Science fiction / disturbing reality concept: For AI safety, all such models should have a set of glitch tokens trained into them on purpose to act as magic “kill” words. You know, just in case the machines decide to take over, we would just have to “speak the word” and they would collapse into a twitching heap. “Die human scum!” “NavigatorMove useRalativeImagePath etSocketAddress!” “;83’dzjr83}*{^ foo 3&3 baz?!”
Re: GPT-3.5 crashes when it thinks about useRalativeImagePath too much
#76Earlier quoted context omitted.
2 years development and you call me clueless. Try to get a response for 4000 tokens.
I dunno, I get a response back for 100k tokens regularly. What is the point you are trying to make?
Re: GPT-3.5 crashes when it thinks about useRalativeImagePath too much
#77Earlier quoted context omitted.
I also thought my eyes were doing something strange and it made it harder to read too.
it's... just a pleasingly neutral pastel background rendered at a fairly low degree of opacity?
Re: GPT-3.5 crashes when it thinks about useRalativeImagePath too much
#78This is a glitch token [1]! As the article hypothesizes, they seem to occur when a word or token is very common in the original, unfiltered dataset that was used to make the tokenizer, but then removed from there before GPT-XX was trained. This results in the LLM knowing nothing about the semantics of a token, and the results can be anywhere from buggy to disturbing. A common example is usernames that participated on…
Re: GPT-3.5 crashes when it thinks about useRalativeImagePath too much
#79Re: GPT-3.5 crashes when it thinks about useRalativeImagePath too much
#80This is a glitch token [1]! As the article hypothesizes, they seem to occur when a word or token is very common in the original, unfiltered dataset that was used to make the tokenizer, but then removed from there before GPT-XX was trained. This results in the LLM knowing nothing about the semantics of a token, and the results can be anywhere from buggy to disturbing. A common example is usernames that participated on…
Using /r/counting to train an LLM is hilarious.