- cross-posted to:
- technology@lemmy.zip
- cross-posted to:
- technology@lemmy.zip
For one month beginning on October 5, I ran an experiment: Every day, I asked ChatGPT 5 (more precisely, its “Extended Thinking” version) to find an error in “Today’s featured article”. In 28 of these 31 featured articles (90%), ChatGPT identified what I considered a valid error, often several. I have so far corrected 35 such errors.
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the tool that is mainly based on wikipedia info?
A tool that gives at least 40% wrong answers, used to find 90% errors?
If you read the post it’s actually quite a good method. Having an LLM flag potential errors and then reviewing them manually as a human is actually quite productive.
I’ve done exactly that on a project that relies on user-submitted content; moderating submissions at even a moderate scale is hard, but having an llm look through for me is easy. I can then check through anything it flags and manually moderate. Neither the accuracy nor precision is perfect, but it’s high enough to be useful so it’s a low-effort way to find a decent number of the thing you’re looking for. In my case I was looking for abusive submissions from untrusted users; in the OP author’s case they were looking for errors. I’m quite sure this method would never find all errors, and as per the article the “errors” it flags aren’t always correct either. But the effort:reward ratio is high on a task that would otherwise be unfeasible.
Bias needs to be reinforced!
legitimate use of a LLM
I find that an extremely simplified way of finding out whether the use of an LLM is good or not is whether the output from it is used as a finished product or not. Here the human uses it to identify possible errors and then verify the LLM output before acting and the use of AI isn’t mentioned at all for the corrections.
The only danger I see is that errors the LLM didn’t find will continue to go undiscovered, but they probably would be undiscovered without the use of the LLM too.
I think the first part you wrote is a bit hard to parse but I think this is related:
I think the problematic part of most genAI use cases is validation at the end. If you’re doing something that has a large amount of exploration but a small amount of validation, like this, then it’s useful.
A friend was using it to learn the linux command line, that can be framed as having a single command at the end that you copy, paste and validate. That isn’t perfect because the explanation could still be off and it wouldn’t be validated but I think it’s still a better use case than most.
If you’re asking for the grand unifying theory of gravity then:
- validation isn’t built into the task (so you’re unlikely to do it with time).
- validation could be as time intensive as the task (so there is no efficiency gain if you validate).
- its beyond your ability to validate so if it says nice things about you then a subset of people will decide the tool is amazing.
Yeah, my morning brain was trying to say that when it is used as a tool by someone that can validate the output and act upon it then it’s often good. When it is used by someone who can’t, or won’t, validate the output and simply uses it as the finished product then it usually isn’t any good.
Regarding your friend learning to use the terminal I’d still recommend validating the output before using it. If it’s asking genAI about flags for ls then sure no big deal, but if a genAI ends up switching around sda and sdb in your dd command resulting in a wiped drive you only got yourself to blame for not checking the manual.
Yes and no. I have enjoyed reading through this approach, but it seems like a slippery slope from this to “vibe knowledge” where LLMs are used for actually trying to add / infer information.
Don’t discard a good technique cause it can be implemented poorly.
