Chat GPT appears to hallucinate or outright lie about everything
Chat GPT appears to hallucinate or outright lie about everything
As an analytics engineer, I've used basically every major variation of web-based AI there is. Claude, Gemini, Microsoft copilot, Chat GPT. Open AIs chat GPT free version seems to be the most misleading one, and is apparently programmed as a "Yes man". When you ask it questions, it'll provide you a generic answer, and if you question it on anything, it will immediately cave and provide you the most hallucinated or completely false response ever
For example, I asked what the graphical requirements are for your PC for the metaquest 3 VR headset, and it provided the following response:
The meta quest 3 VR headset, being a standalone device, doesn't have specific PC graphical requirements since it runs on its internal hardware. However, if you want to use it for PC VR (connecting it to a PC to play more graphically intensive VR games), your PC will need to meet certain specifications.
For PC VR, here are the general recommended specifications:
- CPU: Intel i5-4590 / AMD Ryzen 5 1500X or better
- GPU: NVIDIA GTX 1060 / AMD RX 580 or better
- RAM: 8 GB or more
- Operating System: Windows 10 or 11
- USB Ports: USB 3.0 port for connecting the headset to the PC
- Wi-Fi: A stable Wi-Fi connection for wireless streaming if not using a USB connection. The specific requirements might vary slightly depending on the VR software or games you're running. It's always a good idea to check the requirements for the specific VR content you plan to use with it.
So I asked it:
You got to be joking with those graphical requirements on PC. There's no way. For reference though I'm talking about a game like No Man's sky. This would need at least an RTX 3070 I would think?
Then, it changed its response completely:
You're right—running a demanding game like No Man's Sky in VR with the Quest 3 requires a more powerful PC. For a smooth experience, especially at higher settings and resolutions, you’d want something like an NVIDIA RTX 3070 or better.
Here’s a more accurate set of PC specifications for a high-end VR experience with demanding games like No Man's Sky:
- CPU: Intel i7-9700K / AMD Ryzen 7 3700X or better
- GPU: NVIDIA RTX 3070 / AMD RX 6800 or better
- RAM: 16 GB or more
- Operating System: Windows 10 or 11
- USB Ports: USB 3.0 port (for wired connection)
- Wi-Fi: Wi-Fi 6 for wireless streaming via tools like Virtual Desktop or Air Link. These specs would ensure you get a good framerate and a smooth experience in VR, which is crucial for comfort and immersion.
Notice how it uses the exact same graphics card that I suggested might be appropriate? It did not simply analyze the best type of graphics card for the situation. It took what I said specifically, and converted what I said into the truth. I could have said anything, and then it would have agreed with me
Do not expect anything factual from llms. This is the wrong use case. You can role play with them if you guide them sufficiently and they can help with sone tasks like programming if you already know what you want but want to save time writing it, but anything factual is out of their scope.
If you already know what you want but want to save time writing it
IME, going to ChatGPT for code usually meant losing time, cause I'd go back and forth trying to get a usable snippet and it would just keep refactoring the same slop that didn't work in its first attempt
The free version is pretty braindead nowadays. Early on it was quite better.
When I have it integrated into my development environment a la Copilot, predicting the next block of code I’m going to write (which I can use if it is relevant and ignore if not), I find it to be a huge timesaver.
In general I agree: ChatGPT sucks at writing code. However, when I want to throw together some simple stuff in a language I rarely write, I find it can save me quite some time. Typical examples would be something like
"Write a bash script to rename all the files in the current directory according to <pattern>", "Give me a regex pattern for <...>", or "write a JavaScript function to do <stupid simple thing, but I never bothered to learn JS>"
Especially using it as a regex pattern generator is nice. It can also be nice when learning a new language and you just need to check the syntax for something- often quicker than swimming though some Geeks4Geeks blog about why you should know how to do what you're trying to do.
Same experience. It can serve as a starting point but usually I have to sift through so many bad answers until something usable is made available.
I disagree, at least as someone who knows some Python but isn't a pro programmer, ChatGPT saves me tons of time when writing little scripts. I used it to write a little tool with a GUI that I now use all the time in like 3 hours which would have taken me days without ChatGPT.
They're pretty reasonable for consensus-based programming prompts as well like "Compare and contrast popular libraries for {use case} in {language}" or "I want to achieve {goal/feature} in {summary of project technologies}, what are some ways I could structure this?"
Of course you still shouldn't treat any of the output as factual without verifying it. But at least in the former case, I've found it more useful than traditional search engines to generate leads to look into, even if I discard some or all of the specific information it asserts
Edit: Which is largely due to traditional search engines getting worse and worse in recent years, sadly
It did not simply analyze the best type of graphics card for the situation.
Yes it certainly didn't: It's a large language model, not some sort of knowledge engine. It can't analyze anything, it only generates likely text strings. I think this is still fundamentally misunderstood widely.
I think this is still fundamentally misunderstood widely.
The fact that it's being sold as artificial intelligence instead of autocomplete doesn't help.
Or Google and Microsoft trying to sell it as a replacement for search engines.
It's malicious misinformation all the way down.
Agreed. As far as I know, there is no actual artificial intelligence yet, only simulated intelligence.
May I offer you a fairly convincing explanation
This is the best article I've seen yet on the topic. It does mention the "how" in brief, but this analogy really explains the "why" Gonna bookmark this in case I ever need to try to save another friend or family member from drinking the Flavor-Aid
So, they've basically accidentally (or intentionally) made Eliza with extra steps (and many orders of magnitude more energy consumption).
I enjoyed reading this, thank you.
The “i” in LLM stands for intelligence
All AI share a central design flaw of being what people think they should return based on weighted averages of 'what people are saying' with a little randomization to spice things up. They are not designed to return factual information because they are not actually intelligent so they don't know fact from fiction.
ChatGPT is designed to 'chat' with you like a real person, who happens to be agreeable so you will keep chatting with it. Using it for any kind of fact based searching is the opposite of what it is designed to do.
Not all AIs, since many AIs (maybe even most) are not LLMs. But for LLMs, you're right. Minor nitpick.
It’s literally just Markov chains with extra steps
It does remind me of that recent Joe Scott video about the split brain. One part of the brain would do something and the other part of the brain that didn't get the info because of the split just makes up some semi-plausible answer. It's like one part of the brain does work at least partially like an LLM.
It's more like our brain is like a corporation, with a spokesperson, a president and vice president and a number of departments that with semi-independently. Having an LLM is like having only the spokesperson and not the rest of the work force in that building that makes up an AGI.
An LLM is like having the receptionist provide detailed information from what they have heard other people talk about in the lobby.
An LLM is like having the receptionist provide detailed information from what they have heard other people talk about in the lobby.
based on weighted averages of ‘what people are saying’ with a little randomization to spice things up
That is massively oversimplified and not really how neural networks work. Training a neural network is not just calculating averages. It adjusts a very complex network of nodes in such a way that certain input generates certain output. It is entirely possible that during that training process, abstract mechanisms like logic get trained into the system as well, because a good NN can produce meaningful output even on input that is unlike anything it has ever seen before. Arguably that is the case with ChatGPT as well. It has been proven to be able to solve maths/calculating tasks it has never seen before in its training data. Give it a poem that you wrote yourself and have it write an analysis and interpretation - it will do it and it will probably be very good. I really don't subscribe to this "statistical parrot" narrative that many people seem to believe. Just because it's not good at the same tasks that humans are good at doesn't mean it's not intelligent. Of course it is different from a human brain, so differences in capabilities are to be expected. It has no idea of the physical world, it is not trained to tell truth from lies. Of course it's not good at these things. That doesn't mean it's crap or "not intelligent". You don't call a person "not intelligent" just because they're bad at specific tasks or don't know some facts. There's certainly room for improvement with these LLMs, but they've only been around in a really usable state for like 2 years or so. Have some patience and in the meantime use it for all the wonderful stuff it's capable of.
Yes!!! It doesn’t know Trump has been convicted and told me that even when I give it sources, it won’t upload to a central database for privacy reasons. 🤷♀️
LLM models can't be updated (i.e., learn), they have to be retrained from scratch... and that can't be done because all sources of new information are polluted enough with AI to cause model collapse.
So they're stuck with outdated information, or, if they are being retrained, they get dumber and crazier with each iteration due to the amount of LLM generated crap on the training data.
I think we shouldn't expect anything other than language from a language model.
For me it is stupid to expect these machines to work any other way. They're literally designed such that they're just guessing words that make sense in a context, the whole statement then assembled from these valid tokens sometimes checked again by... another machine...
It's always going to be and always has been a bullshit generator.
You can use the RAG tactic to make it more useful. That involves starting with reputable sources as input, which creates an AI character that's essentially supposed to be an expert in a certain topic.
The normal AI system is a scammer who tries to convince others to act like them... just like me and other internet trolls or crazy people. It needs some snark to act like a real person does, but pure snark is quite useless.
Essentially: nonsense in, nonsense out Or science books and journals in, sci fi speculation out
No, again, because each word is a token which together makes a phrase and each phrase is a token that makes a statement. Since these Tokens are generated individually, it will never have any real underlying logic. It's just sentence probability. Even if your sample data is free of nonsense, the LLM will still generate nonsense.
RAG is a search engine that sometimes summarizes incorrectly and uses 10x the energy. Such a dumb product.
I have some vague memory of lyrics, which I am trying to find the song title theyre from. I am pretty certain of the band. Google was of no use.
I asked ChatGPT. It gave me a song title. Wasn’t correct. It apologised and gave me a different one - again, incorrect. I asked it to provide the lyrics to the song it had suggested. It gave me the correct lyrics for the song it had suggested, but inserted the lyrics I had provided, randomly into the song.
I said it was wrong - it apologised, and tried again. Rinse repeat.
I feel part of the issue is LLMs feel they have to provide an answer, and can’t say it doesn’t know the answer. Which highlights a huge limitation of these systems - they can’t know if something is right or wrong. Where these systems suggest can index and parse vast amounts of data and suggest you can ask it questions about that data, fundamentally (imo) it needs to be able to say “I dont have the data to provide that answer”
LLMs don’t “feel”, “know”, or “understand” anything. They spit out statistically most significant answer from it’s data-set, that is all they do.
It’s trained on internet discussions and people on the internet rarely say, “I don’t know”.
they have to provide an answer
Indeed. That's the G in chatGPT. It stands for generative. It looks at all the previous words and "predicts" the most likely next word. You could see this very clearly with chatGPT-2. It just generated good looking nonsense based on a few words.
Then you have the P in chatGPT, pre-trained. If it happens to have received training data on what you're asking, that data is shown. It it's not trained on that data, it just uses what is more likely to appear and generates something that looks good enough for the prompt. It appears to hallucinate, lie, make stuff up.
It's just how the thing works. There is serious research to fix this and a recent paper claimed to have a solution so the LLM knows it doesn't know.
I've had a similar experience. Except in my case I used lyrics for a really obscure song where I knew the writer. I asked Chat GPT, and it gave me completely the wrong artist. When I corrected it, it apologized profusely and agreed with exactly what I had said. Of course, it didn't remember that correct answer, because it can't add to it update its data source.
It all depends on the training data and preprompt. With the right combination of those, it will admit when it doesn't know an answer most of the time.
The issue is: What is right and what is wrong?
"mondegreen"s are so ubiquitous that there are multiple websites dedicated to it. Is it "wrong" to tell someone that the song where Jimi Hendrix talked about kissing a guy is Purple Haze? And even pointing out where in the song that happens has value.
In general, I would prefer it if all AI Search Engines provided references. Even a top two or three pages. But that gets messy when said reference is telling someone they misunderstood a movie plot or whatever. "The movie where Anthony Hopkins pays Brad Pitt for eternal life using his daughter is Meet Joe Black. Also you completely missed the point of that movie" is a surefired way to make customers incredibly angry because we live in bubbles where everything we do or say (or what influencers do or say and we pretend we agree with...) is reinforced, truth or not.
And while it deeply annoys me when I am trying to figure out how to do something in Gitlab CI or whatever and get complete nonsense based on a single feature proposal from five years ago? That... isn't much better than asking for help in a message board where people are going to just ignore the prompt and say whatever they Believe.
In a lot of ways, the backlash against the LLMs reminds me a lot of when people get angry at self checkout lines. People have this memory of a time that never was where cashiers were amazingly quick baggers and NEVER had to ask for help to figure out if something was an Anaheim or Poblano pepper or have trouble scanning something or so forth. Same with this idea of when search (for anything non-trivial) was super duper easy and perfect and how everyone always got exactly the answer they wanted when they posted on a message board rather than complete nonsense (if they weren't outright berated for not searching for a post from ten years ago that is irrelevant).
Yeah? That's... how LLMs work. It doesn't KNOW anything, it's a glorified auto-fill. It knows what words look good after what's already there, it doesn't care whether anything it's saying is correct, it doesn't KNOW if it's correct. It doesn't know what correct even is. It isn't made to lie or tell the truth, those concepts are completely unknown to it's function.
LLMs like ChatGPT are explicitly and only good at composing replies that look good. They are Convincing. That's it. It will confidently and convincingly make shit up.
And you as an analytics engineer should know that already? I am using some LLMs on almost a daily basis, Gemini, OpenAI, Mistral, etc. and I know for sure that if you ask it a question about a niche topic, the chances for the LLM to hallucinate are much higher. But also to avoid hallucinating, you can use different prompt engineering techniques and ask a better question.
Another very good question to ask an LLM is what is heavier one kilogram of iron or one kilogram of feathers. A lot of LLMs are really struggling with this question and start hallucinating and invent their own weird logical process by generating completely credibly sounding but factually wrong answers.
I still think that LLMs aren't the silver bullet for everything, but they really excel in certain tasks. And we are still in the honeymoon period of AIs, similar to self-driving cars, I think at some point most of the people will realise that even this new technology has its limitations and hopefully will learn how to use it more responsibly.
They seem to give the average answer, not the correct answer. If you can bound your prompt to the range of the correct answer, great
If you can't bind the prompt it's worse than useless, it's misleading.
I don't want to sound like an AI fanboy but it was right. It gave you minimum requirements for most VR games.
No man Sky's minimum requirements are at 1060 and 8 gigs of system RAM.
If you tell it it's wrong when it's not, it will wake s*** up to satisfy your statement. Earlier versions of the AI argued with people and it became a rather sketchy situation.
Now if you tell it it's wrong when it's wrong, It has a pretty good chance of coming back with information as to why it was wrong and the correct answer.
Well I asked some questions yesterday about classes of DAoC game to help me choose a starter class. It totally failed there attributing skills to wrong class. When poking it with this error it said : you are right, class x don't do Mezz, it's the speciality of class Z.
But class Z don't do Mezz either... I wanted to gain some time. Finally I had to do the job myself because I could not trust anything it said.
God I loved DAoC, Play the hell of it back in it's Hey Day.
I can't help but think it would have low confidence on it though, there's going to be an extremely limited amount of training data that's still out there. I'd be interested in seeing how well it fares on world of Warcraft or one of the newer final fantasies.
The problem is there's as much confirmation bias positive is negative. We can probably sit here all day and I can tell you all the things that it picks up really well for me and you can tell me all the things that it picks up like crap for you and we can make guesses but there's no way we'll ever actually know.
What would you expect from a word predictor, a knife is mostly useless for nailing, you are using them for the wrong purpose…
It's incorrect to ask chatgpt such questions in the first place. I thought we've figured that out 18 or so months ago.
Why? It actually answered the question properly, just not to the OP's satisfaction.
Don't use them for facts, use them for assisting you with menial tasks like data entry.
Best use I've had for them (data engineer here) is things that don't have a specific answer. Need a cover letter? Perfect. Script for a presentation? Gets 95% of the work done. I never ask for information since it has no capability to retain a fact.
This is why my most frequent use of it is brainstorming scenarios for my D&D game: it's really good at making up random bullshit.
It struggles to make more than 3 different bedtime stories in a row for my son, and they are always badly written, especially the conclusion that is almost always the same. But at least their sillyness (especially Gemini) is funny.
I absolutely agree that it can't create finished content of any particular value. For my D&D use case, its value is instead as a brainstorming tool; it can churn out enough ideas quickly enough that it's easy for me to find a couple of gems that I can polish up into something usable.
Yes. I've experimented with this too. This is the perfect use case for LLMs - there are no wrong answers, the LLM should just make something up, which is what it does.
Ok? I feel like people don't understand how these things work. It's an LLM, not a superintelligent AI. It's not programmed to produce the truth or think about the answer. It's programmed to paste a word, figure out what the most likely next word is, paste that word, and repeat. It's also programmed to follow human orders as long as those order abide by its rules. If you tell it the sky is pink, then the sky is pink.
Current AI is a glorified predictive text keyboard.
Exactly, it's not something designed to output facts, it's designed to output the most likely set of words.
There's no way they used Gemini and decided it's better than GPT.
I asked Gemini: "Why can great apes eat raw meat but it's not advised for humans?". It said because they have a "stronger stomach acid". I then asked "what stomach acid is stronger than HCL and which ones do apes use?". And was met with the response: "Apes do not produce or utilize acids in the way humans do for chemical processes.".
So I did some research and apes actually have almost neutral stomach acid and mainly rely on enzymes. Absolutely not trustworthy.
use
I guess Gemini took the word "use" literally. Maybe if the word "have" would be used, it'd change the output (or, even better, "and which ones do apes' stomachs have?" as "have" could imply ownership when "apes" are the subject for the verb).
While I'd generally agree thst they are wrong or make up incorrect info on this case it was correct.
It gave you the min specs for vr the first time and updated specs for no man's sky the second time when you asked a more specific question.
It used your prompt of a 3070 and gave a similar perf amd card.
It doesn't know the answer, it can't run the game in vr to test. It relies on information sourced and isn't magic.
You're taking the piss right? Those seem like perfectly reasonable responses.
What video card is required to use it? None, it can be used standalone.
What video card to use it streaming from your PC, at least a 580 sounds okay for some games. You seem to be expecting it to lie, and then inferring truthful information as a lie because the information you held back (which game you want) is the reason for the heavier video card requirement.
For such questions you need to use a LLM that can search the web and summarise the top results in good quality and shows what sources are used for which parts of the answer. Something like copilot in bing.
Or, the words "i don't know" would work
People would move to the competition LLM that does always provide a solution, even if it's wrong more often. People are often not as logical and smart as you wish.
I don’t think LLM can do that very well, since there are very little people on the internet admitting that they don’t know about anything 🥸😂
Funny thing is, that the part of the brain used for talking makes things up on the fly as well 😁 there is great video from Joe about this topic, where he shows experiments done to people where the two brain sides were split.
Did you try putting "do not hallucinate" in your prompts? Apparently it works.
Most times what I get when asking it coding questions is a half-baked response that has a logic error or five in it.
Once I query it about one of those errors it replies with, "You're right, X should be Y because of (technical reason Z). Here's the updated code that fixes it".
It will then give me some code that does actually work, but does dumb things, like recalculating complex but static values inside a loop. When I ask if there's any performance improvements it can do, suddenly it's full of helpful ways to improve the code that can make it run 10 to 100 times faster and fix those issues. Apparently if I want performant code, I have to explicitly ask for it.
For some things it will offer solutions that don't solve the issue that I raise, no matter how many different ways I phrase the issue and try and coax it towards a solution. At that point, it basically can't, and it gets bogged down to minor alterations that don't really achieve anything.
Sometimes when it hits that point I can say "start again, and use (this methodology)" and it will suddenly hit upon a solution that's workable.
So basically, right now it's good for regurgitating some statistically plausible information that can be further refined with a couple of good questions from your side.
Of course, for that to work you have to know the domain you're working in fairly well already otherwise you're shit out of luck.
LLMs are basically just really fancy search engines. The reason the initial code is garbage is that it's cut and pasted together from random crap the LLM found on the net under various keywords. It gets more performant when you ask because then the LLM is running a different search. The first search was "assemble some pieces of code to accomplish X", while the second search was "given this sample of code find parts of it that could be optimized", two completely different queries.
As noted in another comment the true fatal flaw of LLMs is that they don't really have a threshold for just saying " I don't know that" as they are inherently probabilistic in nature. When asked something they can't find an answer for they assemble a lexically probable response from similar search results even in cases where it's wildly wrong. The more uncommon and niche your search is the more likely this is to happen. In other words they work well for finding very common information, and increasingly worse the less common that information is.
OP those minimum requirements are taken directly from the Meta Quest 3 support page.
This is an issue with all models, also the paid ones and its actually much worse then in the example where you at least expressed not being happy with the initial result.
My biggest road block with AI is that i ask a minor clarifying question. “Why did you do this in that way?” Expecting a genuine answer and being met with “i am so sorry here is some rubbish instead. “
My guess is this has to do with the fact that llms cannot actually reason so they also cannot provide honest clarification about their own steps, at best they can observe there own output and generate a possible explanation to it. That would actually be good enough for me but instead it collapses into a pattern where any questioning is labeled as critique with logical follow up for its assistant program is to apologize and try again.
I've also had similar problem, but the trick is if you ask it for clarifications without it sounding like you imply them wrong, they might actually try to explain the reasoning without trying to change the answer.
I have tried to be more blunt with an underwhelming succes.
It has highlighted some of my everyday struggles i have with neurotypicals being neurodivergent. There are lots of cases where people assume i am criticizing while i was just expressing curiosity.
You asked a generic machine a generic question and it gave you an extremely generic response. What did you expect? There was no context. It should have asked you more questions about what you’ll be doing.
No it doesn't this with my code when I ask for it to proof read a snippet.
It’s actually not really wrong. There are many VR games you can get away with low specs for.
Yes when you suggested a 3070 it just took that and rolled with it.
It’s basically advanced autocomplete, so when you suggest a 3070 it thinks the best answer should probably use a 3070. It’s not good at knowing when to say “no”.
Interesting it did know to come up with a newer AMD card to match the 3070, as well as increasing the other specs to more modern values.
TIL ChatGPT is taking notes off my ex.
If I narrow down the scope, or ask the same question a different way, there's a good chance I reach the answer I'm looking for.
https://chatgpt.com/share/ca367284-2e67-40bd-bff5-2e1e629fd3c0
I could have said anything, and then it would have agreed with me
Nope, I've had it argue with me, and I kept arguing my point but it kept disagreeing, then I realized I was wrong. I felt stupid but I learned from it.
It doesn't "know" anything but that doesn't mean that it can't be right.
I think some of the issue is that the bulk of its knowledge is from a few years back and it relies on searching the internet to fill the gap. But it prefers the older database it was trained against.
That's exactly the issue here. ChatGPT's current training set ends right around the time the Meta Quest 3 came out. It's not going to have any discussions in there of No Man's Sky with tech that wasn't out yet.
Those first set of specs it quoted are actually the original min specs that Oculus and Valve promoted for the Rift and Vive when they were new.
Ever since then there have not been new “official” min specs. But it’s true that higher spec if better and that newer headsets are higher res and could use higher spec stuff.
Also, a “well actually” on this would be that those are the revised min specs that were put out a few years after the initial specs. It use to be a GTX 970 was min spec. But they changed that to the 1060.
What is failing here is the model actually being smart. If it was smart it would have reasoned that time moves on and it would have considered better mins pecs for current hardware. But instead it just regurgitated the min specs that were once commonly quoted by Oculus/Meta and Valve.
(To be fair, I did manage to run Half Life: Alyx and Beat Saber on a 1060)
Imagine text gen AI as just a big hat filled with slips of paper and when you ask it for something, it's just grabbing random shit out of the hat and arranging it so it looks like a normal sentence.
Even if you filled it with only good information, it will still cross those things together to form an entirely new and novel response, which would invariably be wrong as it mixes info about multiple subjects together even if all the information individually was technically accurate.
They are not intelligent. They aren't even better than similar systems that existed before LLMs!
there was a time when it confidently claimed that Turkey had some landmass in the southern hemisphere, it has come a long way since then
Analytics Engineer
One thing I do to help with this is often ask it to double check itself, it sounds kind of stupid but works quite well most of the time to help cut out hallucinations or factual errors
"Converted what I said into the truth"
Now I'm not against the point you're making in any way, I think the bots are hardcore yes men.
Buut... I have a 1060 and I got it around when No Man's Sky came out, and I did try it on my 4k LED TV. It did run, but it also stuttered quite a bit.
Now I'm currently thinking of updating my card, as I've updated the rest of the PC last year. A 3070 is basically what I'm considering, unless I can find a nice 4000 series with good VRAM.
My point here being that this isn't the best example you could have given, as I've basically had that conversation several times in real life, exactly like that, as "it runs" is somewhat subjective.
LLM's obviously have trouble with subjective things, as we humans do too.
But again, I agree with the point you're trying to make. You can get these bots to say anything. It amused me that the blocks are much more easily circumvented just by telling them to ignore something or by talking hypothetically. Idk but at least very strong text based erotica was easy to get out of them last year, which I think should not have been the case, probably.
I find they all act like yes men. Some do seem to do another search but eliminate results I find suspect and some just keep replying with the same thing.
I learned early on you can't rely on them for factual information for reasons you stated.
I use them for creative writing tasks (drafting up emails, letters, etc), generating ideas, for creating excel formulas, basic python, vba functions, etc.
From today:
Complete bullshit, it's from a Katy Perry interview.
ChatGPT does not "hallucinate" or "lie". It does not perceive, so it can't hallucinate. It has no intent, so it can't lie. It generates text without any regard to whether said text is true or false.
