The problem is the same prompt will yield good results one time and bad results another. The "get better at prompting" is often just an excuse for AI hallucination. Better prompting can help but often it's totally fine, the tech is just not there yet.
While this is true, I have seen this happen enough times to confidently bet all my money that OP will not return and post a link to their incorrect ChatGPT response.
Seemingly basic asks that LLMs consistently get wrong have lots of value to people because they serve as good knowledge/functionality tests.
I don't have to post my chat, someone else already posted a chat claiming ChatGPT gave them correct answers when the answers ChatGPT gave them were all kinds of wrong.
As far as I can tell these are all real products and all meet the requirement of having fluoride and being SLS free.
Since you did return however and that was half my bet, I suppose you are still entitled to half my life savings. But the amount is small so maybe the knowledge of these new toothpastes is more valuable to you anyway.
If you want a correct answer the first time around, and give up if you don't get it, even if you know the thing can give it to you with a bit more effort (but still less effort than searching yourself), don't you think that's a user problem?
I briefly got excited about the possibility of local LLMs as an offline knowledge base. Then I tried asking Gemma for a list of the tallest buildings in the world and it just made up a bunch. It even provided detailed information about the designers, year of construction etc.
I still hope it will get better. But I wonder if an LLM is the right tool for factual lookup - even if it is right, how do I know?
I wonder how quickly this will fall apart as LLM content proliferates. If it’s bad now, how bad will it be in a few years when there’s loads of false but credible LLM generated blogspam in the training data?
> I wonder how quickly this will fall apart as LLM content proliferates. If it’s bad now, how bad will it be in a few years when there’s loads of false but credible LLM generated blogspam in the training data?
There is already misinformation online so only the marginal misinformation is relevant. In other words do LLMs generate misinformation at a higher rate than their training set?
For raw information retrieval from the training set misinformation may be a concern but LLMs aren’t search engines.
Emergent properties don’t rely on facts. They emerge from the relationship between tokens. So even if an LLM is trained only on misinformation abilities may still emerge at which point problem solving on factual information is still possible.
The person that started this conversation verified the answers were incorrect. So it sounds like you just do that. Check the results. If they turn out to be false, tell the LLM or make sure you're not on a bad one. It still likely to be faster than searching yourself.
That's all well and good for this particular example. But in general, the verification can often be so much work it nullifies the advantage of the LLM in the first place.
Something I've been using perplexity for recently is summarizing the research literature on some fairly specific topic(e.g. the state of research on the use of polypharmacy in treatment of adult ADHD). Ideally it should look up a bunch of papers, look at them and provide a summary of the current consensus on the topic. At first, I thought it did this quite well. But I eventually noticed that in some cases it would miss key papers and therefore provide inaccurate conclusions. The only way for me to tell whether the output is legit is to do exactly what the LLM was supposed to do; search for a bunch of papers, read them and conclude on what the aggregate is telling me. And it's almost never obvious from the output whether the LLM did this properly or not.
The only way in which this is useful, then, is to find a random, non-exhaustive set of papers for me to look at(since the LLM also can't be trusted to accurately summarize them). Well, I can already do that with a simple search in one of the many databases for this purpose, such as pubmed, arxiv etc. Any capability beyond that is merely an illusion. It's close, but no cigar. And in this case close doesn't really help reduce the amount of work.
This is why a lot of the things people want to use LLMs for requires a "definiteness" that's completely at odds with the architecture. The fact that LLMs are food at pretending to do it well only serves to distract us from addressing the fundamental architectural issues that need to be solved. I think think any amount of training of a transformer architecture is gonna do it. We're several years into trying that and the problem hasn't gone away.
> The only way for me to tell whether the output is legit is to do exactly what the LLM was supposed to do; search for a bunch of papers, read them and conclude on what the aggregate is telling me. And it's almost never obvious from the output whether the LLM did this properly or not.
You're describing a fundamental and inescapable problem that applies to literally all delegated work.
Sure, if you wanna be reductive, absolutist and cynical about it. What you're conveniently leaving out though is that there are varying degrees of trust you can place in the result depending on who did it. And in many cases with people, the odds they screwed it up are so low they're not worth considering. I'm arguing LLMs are fundamentally and architecturally incapable of reaching that level of trust, which was probably obvious to anyone interpreting my comment in good faith.
I think what you're leaving is that what you're applying to people also applies to LLMs. There are many people you can trust to do certain things but can't trust to do others. Learning those ropes requires working with those people repeatedly, across a variety of domains. And you can save yourself some time by generalizing people into groups, and picking the highest-level group you can in any situation, e.g. "I can typically trust MIT grads on X", "I can typically trust most Americans on Y", "I can typically trust all humans on Z."
The same is true of LLMs, but you just haven't had a lifetime of repeatedly working with LLMs to be able to internalize what you can and can't trust them with.
Personally, I've learned more than enough about LLMs and their limitations that I wouldn't try to use them to do something like make an exhaustive list of papers on a subject, or a list of all toothpastes without a specific ingredient, etc. At least not in their raw state.
The first thought that comes to mind is that a custom LLM-based research agent equipped with tools for both web search and web crawl would be good for this, or (at minimum) one of the generic Deep Research agents that's been built. Of course the average person isn't going to think this way, but I've built multiple deep research agents myself, and have a much higher understanding of the LLMs' strengths and limitations than the average person.
So I disagree with your opening statement: "That's all well and good for this particular example. But in general, the verification can often be so much work it nullifies the advantage of the LLM in the first place."
I don't think this is a "general problem" of LLMs, at least not for anyone who has a solid understanding of what they're good at. Rather, it's a problem that comes down to understanding the tools well, which is no different than understanding the people we work with well.
P.S. If you want to make a bunch of snide assumptions and insults about my character and me not operating in good faith, be my guest. But in return I ask you to consider whether or not doing so adds anything productive to an otherwise interesting conversation.
Yup, and worse since the LLM gives such a confident sounding answer, most people will just skim over the ‘hmm, but maybe it’s just lying’ verification check and move forward oblivious to the BS.
People did this before LLMs anyway. Humans are selfish, apathetic creatures and unless something pertains to someone's subject of interest the human response is "huh, neat. I didn't know dogs could cook pancakes like that" then scroll to the next tiktok.
This is also how people vote, apathetically and tribally. It's no wonder the world has so many fucking problems, we're all monkeys in suits.
Sure, but there's degrees in the real world. Do people sometimes spew bullshit (hallucinate) at you? Absolutely. But LLMs, that's all they do. They make bullshit and spew it. That's their default state. They're occasionally useful despite this behavior, but it doesn't mean that they're not still bullshitting you
It depends on whether the cost of search or of verification dominates.
When searching for common consumer products, yeah, this isn't likely to help much, and in a sense the scales are tipped against the AI for this application.
But if search is hard and verification is easy, even a faulty faster search is great.
I've run into a lot of instances with Linux where some minor, low level thing has broken and all of the stackexchange suggestions you can find in two hours don't work and you don't have seven hours to learn about the Linux kernel and its various services and their various conventions in order to get your screen resolutions correct, so you just give up.
Being in a debug loop in the most naive way with Claude, where it just tells you what to try and you report the feedback and direct it when it tunnel visions on irrelevant things, has solved many such instances of this hopelessness for me in the last few years.
So instead of spending seven hours to get at least an understanding how the Linux kernel work and the interaction of various user-land programs, you've decided to spend years fumbling in the dark and trying stuff every time an issue arises?
I would like to understand how you ideally imagine a person solving issues of this type. I'm for understanding things instead of hacking at them in general, and this tendency increases the more central the things to understand are to the things you like to do. However, it's a point of common agreement that just in the domain of computer-related tech, there is far more to learn than a person can possibly know in a lifetime, and so we all have to make choices about which ones we want to dive into.
I do not expect to go through the process I just described for more than a few hours a year, so I don't think the net loss to my time is huge. I think that the most relevant counterfactual scenario is that I don't learn anything about how these things work at all, and I cope with my problem being unfixed. I don't think this is unusual behavior, to the degree that it's I think a common point of humor among Linux users: https://xkcd.com/963/https://xkcd.com/456/
This is not to mention issues that are structurally similar (in the sense that search is expensive but verification is cheap, and the issue is generally esoteric so there are reduced returns to learning) but don't necessarily have anything to do with the Linux kernel: https://github.com/electron/electron/issues/42611
I wonder if you're arguing against a strawman that thinks that it's not necessary to learn anything about the basic design/concepts of operating systems at all. I think knowledge of it is fractally deep and you could run into esoterica you don't care about at any level, and as others in the thread have noted, at the very least when you are in the weeds with a problem the LLM can often (not always) be better documentation than the documentation. (Also, I actually think that some engineers do on a practical level need to know extremely little about these things and more power to them, the abstraction is working for them.)
Holding what you learn constant, it's nice to have control about in what order things force you to learn them. Yak-shaving is a phenomenon common enough that we have a term for it, and I don't know that it's virtuous to know how to shave a yak in-depth (or to the extent that it is, some days you are just trying to do something else).
More often than not, the actual implementation is more complex than the theory that outlines it (think Turing Machine and today's computer). Mostly because the implementation is often the intersection of several theories spanning multiple domain. Going at a problem at a whole is trying to solve multiple equations with a lot of variables and it's an impossible task for most. Learning about all the domains is also a daunting tasks (and probably fruitless as you've explained it).
But knowing the involved domain and some basic knowledge is easy to do and more than enough to quickly know where to do a deep dive. Instead of relying on LLMs that are just giving plausible mashup on what was on their training data (which is not always truthful).
I still remember when Altavista.digital and excite.com where brand new. They were revolutionary and very useful,even if they couldn't find results for all the prompts we made.
I am unconvinced that searching for this yourself is actually more effort than repeatedly asking the Mighty Oracle of Wrongness and cross-checking its utterances.
