It will almost always be detectable if you just read what is written. Especially for academic work. It doesn’t know what a citation is, only what one looks like and where they appear. It can’t summarise a paper accurately. It’s easy to force laughably bad output by just asking the right sort of question.
The simplest approach for setting homework is to give them the LLM output and get them to check it for errors and omissions. LLMs can’t critique their own work and students probably learn more from chasing down errors than filling a blank sheet of paper for the sake of it.
Some things are inherent in the way the current LLM’s work. It doesn’t reason, it doesn’t understand, it just predicts the next word out of likely candidates based on the previous words. It can’t look ahead to know if it’s got an answer, and it can’t backtrack to change previous words if it later finds out it’s written itself into a corner. It won’t even know it’s written itself into a corner, it will just continue predicting in the pattern it’s seen, even if it makes little or no sense for a human.
It just mimics the source data it’s been trained on, following the patterns it’s learned there. At no point does it have any sort of understanding of what it’s saying. In some ways it’s similar to this, where a man learned how enough french words were written to win the national scrabble competition, without any clue what the words actually mean.
And until we get a new approach to LLM’s, we can only improve it by adding more training data and more layers allowing it to pick out more subtle patterns in larger amounts of data. But with the current approach, you can’t guarantee that what it writes will be correct, or even make sense.
This is not entirely correct, in my experience. With the current version pf gtp-4 you might be right, but the initial versions were extremely good. Clearly you have to work with it, you cannot ask for the whole work
I meant initial versions of chatGTP 4. ChatGTP isn’t lying, simply because lying implies a malevolent intent. Gtp-4 has no intent, it just provides an output given an input, that can be either wrong or correct. A model able to provide more correct answers is a more accurate model. Computing accuracy for a LLM is not trivial, but gpt-4 is still a good model. User has to know how to use it, what to expect and how to evaluate the result. If they are unable to do so it’s completely their fault.
I think there’s a big difference between being able to identify an AI by talking to it and being able to identify something written by an AI, especially if a human has looked over it for obvious errors.
It will almost always be detectable if you just read what is written. Especially for academic work. It doesn’t know what a citation is, only what one looks like and where they appear. It can’t summarise a paper accurately. It’s easy to force laughably bad output by just asking the right sort of question.
The simplest approach for setting homework is to give them the LLM output and get them to check it for errors and omissions. LLMs can’t critique their own work and students probably learn more from chasing down errors than filling a blank sheet of paper for the sake of it.
given how much AI has advanced in the past year alone, saying it will “always” be easy to spot is extremely short sighted.
Some things are inherent in the way the current LLM’s work. It doesn’t reason, it doesn’t understand, it just predicts the next word out of likely candidates based on the previous words. It can’t look ahead to know if it’s got an answer, and it can’t backtrack to change previous words if it later finds out it’s written itself into a corner. It won’t even know it’s written itself into a corner, it will just continue predicting in the pattern it’s seen, even if it makes little or no sense for a human.
It just mimics the source data it’s been trained on, following the patterns it’s learned there. At no point does it have any sort of understanding of what it’s saying. In some ways it’s similar to this, where a man learned how enough french words were written to win the national scrabble competition, without any clue what the words actually mean.
And until we get a new approach to LLM’s, we can only improve it by adding more training data and more layers allowing it to pick out more subtle patterns in larger amounts of data. But with the current approach, you can’t guarantee that what it writes will be correct, or even make sense.
This is not entirely correct, in my experience. With the current version pf gtp-4 you might be right, but the initial versions were extremely good. Clearly you have to work with it, you cannot ask for the whole work
That’s not true! There’s heaps of early-GPT articles pointing out how much bullshit it regurgitates (eg Why does ChatGPT constantly lie?). And no evidence at all that the breathless fanboys have even stopped to check.
I meant initial versions of chatGTP 4. ChatGTP isn’t lying, simply because lying implies a malevolent intent. Gtp-4 has no intent, it just provides an output given an input, that can be either wrong or correct. A model able to provide more correct answers is a more accurate model. Computing accuracy for a LLM is not trivial, but gpt-4 is still a good model. User has to know how to use it, what to expect and how to evaluate the result. If they are unable to do so it’s completely their fault.
Why are you so pissed of a good nlp model?
I think there’s a big difference between being able to identify an AI by talking to it and being able to identify something written by an AI, especially if a human has looked over it for obvious errors.