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There is no chance whatsoever that any tool will ever be able to reliably tell the difference between LLM and human content, and I can’t understand how anyone t

by CPLX 3y ago

There is no chance whatsoever that any tool will ever be able to reliably tell the difference between LLM and human content, and I can’t understand how anyone thinks such a thing is possible. There's no mechanism of action for such a thing. The information would have to be encoded in the text and it isn't. Honestly the best way to think about it is to invoke the infinite number of monkeys scenario since believing this requires you to disprove the infinite monkeys theorem. Consider this thought experiment. 1) We will start with a piece of text that your detector is 100% certain was created by a GPT tool. 2) Now, prove that there is no way whatsoever for at least one human being to independently create this text. If you can’t prove that, then your tool is bullshit. cite: https://en.wikipedia.org/wiki/Infinite_monkey_theorem https://en.wikipedia.org/wiki/Infinite_monkey_theorem

vidarh 3y ago
There's a whole population out there that is fundamentally convinced that humans have some innate superiority over LLMs in terms of reasoning that will buy into this because they believe there will always be a difference for any text of meaningful length. My main counter to that is that any such detector can only keep working if it's inaccessible to people directly or indirectly without huge consequences, because if it's accessible, it'll be probed and used to produce test cases to improve LLMs with and/or to train models to "spin"/postprocess LLM output. The latter can be done much "cheaper", e.g. consider the article: > Real science papers also use more equivocal language – like "however", "but", "although" as well as "this" and "because". Even if you can't circumvent this just with careful prompting (and you probably can in this specific case), this kind of information alone is enough to know you need to adjust the frequency of those words to better match scientific papers, whether by fine-tuning on some science papers or training a small model to know when it's ok to interject them in a text. At a minimum you need to host and rate limit access to such a detector, or you can just automate working around it.

by CPLX 3y ago

vidarh 3y ago

There's a whole population out there that is fundamentally convinced that humans have some innate superiority over LLMs in terms of reasoning that will buy into this because they believe there will always be a difference for any text of meaningful length. My main counter to that is that any such detector can only keep working if it's inaccessible to people directly or indirectly without huge consequences, because if it's accessible, it'll be probed and used to produce test cases to improve LLMs with and/or to train models to "spin"/postprocess LLM output. The latter can be done much "cheaper", e.g. consider the article: > Real science papers also use more equivocal language – like "however", "but", "although" as well as "this" and "because". Even if you can't circumvent this just with careful prompting (and you probably can in this specific case), this kind of information alone is enough to know you need to adjust the frequency of those words to better match scientific papers, whether by fine-tuning on some science papers or training a small model to know when it's ok to interject them in a text. At a minimum you need to host and rate limit access to such a detector, or you can just automate working around it.