It's like generating code in a language that you know nothing about. You should check for bugs, but you can't.
It's not perceiving reality incorrectly, it's presenting wholesale fiction as fact both coherently and with absolute confidence. It even forges supporting documentation ad-hoc.
GPT is not a poor schizophrenic suffering from delusions or innocuous "hallucinations." It is the world's most advanced liar.
Lies, BS, and Con artistry all require conscious motive and intent. Thats a bridge to far, for me, in ascribing ‘intelligence’ to these models.
Hallucination, to me, conveys ‘seeing things (facts) that are not there’. To the extent the models are ‘perceiving’, they ARE perceiving reality incorrectly. Granted, I expect many times it’s because the source of the model training data are, at best, just wrong or are lying.
These are worse as they imply the thing generating the words knows the truth and purposely says something else.
An LLM is just doing next token prediction. It's a mathematical process. It's not trying to "hide" the truth from you.
Besides,
> it's presenting wholesale fiction as fact both coherently and with absolute confidence
That is not in any way distinct from perceiving reality incorrectly. It is a symptom common to both skilled lying and hallucination.
Confabulation is the unintended generation of false memories.
Hallucination is false perception.
Clearly, the phenomenon we are seeing with LLM researchers call Hallucination better fits Confabulation.
IMO, so long as you're aware the information is often subtly wrong, it's not that different from, e.g., physics classes progressively lying to you less to allow your brain to build a framework to house the incoming ideas.
I do not use ChatGPT as a search engine. Its ability to confidently hallucinate consistently places it much below a human expert on any topic that I care to understand correctly.
I don’t think it will cost me much to not use the explicitly-not-a-search-engine thing as a search engine.
Which LLM will you use to verify that ChatGPT is more knowledgeable than human experts on a given topic?
They are both insanely powerful tools, and like most insanely powerful tools, the hazards are considerable.
This is a weird thing to write to a stranger. I suppose there will be no need to caution people about rudeness or making strange assumptions in the utopian future where humans only talk to chatbots, though.
Of course, it will be trivial for such bots to emulate humans if they find that useful.
Fun times.
"I do not use ChatGPT as a search engine. Its ability to confidently hallucinate consistently places it much below a human expert on any topic that I care to understand correctly."
:)
Thank goodness that I didn’t do that, I’d certainly have egg on my face if I hadn’t included myself in the joke and somebody called me out on it!
There is a rather substantial difference between a search engine, which suggests sources which the reader can evaluate based on their merits, and a language model, whose output may or may not be based on any sources at all, and which cannot (accurately) cite sources for statements it makes.
> Similar degrees of caution and skepticism must be applied to results from both ML and traditional search engines.
This is a fairly ridiculous statement.
Really? Have you used Google lately -- say, in the past 6-12 months?
If a person is in the habit of using a search engine like a chat bot by typing in questions AskJeeves-style and then believing what text pops up in the info cards above the ads (which are themselves above the search results), I could see how the distinction between chat bots and search engines could seem trivial.
The similarity between chat bots and search engines breaks down significantly if the user scrolls down past the info cards and ads and then clicks on a link to an external website. At that point in the user experience it is no longer like chatting with a confident NPC.
I also think people who say that search engines lie are seriously overestimating the amount of lies on returned by a search result. Social media is one thing but the broader internet is filled with articles from relatively reputable sources. When I Google "what is a large language model" my top results (there aren't even ads on this particular query to really muddle things) are:
1. Wikipedia
Sure this is the most obvious place for lies but we already understand that. Moreover, the people writing the text have some notion of what is true and false unlike an LLM. I can always also use the links it provides.
2. Nvidia
Sure they have a financial motive to promote LLMs but I don't see a reason they have to outright mislead me. They also happen to publish a significant amount of ML research so probably a good source.
3. TechTarget
I don't know this source well but their description seems to agree deeply with the other two so I can be relatively sure on both this and the others' accuracy. It's a really similar story with Bing. I can also look for sources that cite specific people like a sourced Forbes article that interviews people from an LLM company.
With multiple sources, I can also build a consensus on what an LLM is and reach out further. If I really want to be sure I can type a site:edu to just double check. When I have the source and the text I can test both agreement with consensus and weigh the strength of a source. I can't do that with an LLM since it's the same model when you reprompt. I get that LLMs can give a good place to begin by giving you keywords and phrases to search but it's a really, really poor replacement for search or for learning stuff you don't have experience in.
My advice to folks is, if you actually want to know how this stuff works at some basic level, put in some time learning how basic linear and logistic regression work, including how to train it using back propagation. From there you'll have a solid foundation that gives enough context to understand most deep learning concepts at a high level.
when it can hallucinate content, why do that instead of reading a blog post from an expert?
If you don't trust its memory, copy a piece of high quality text in the topic of interest inside the context, as reference.
To learn more deeply though, get started with getting it to work and when you are curious or something doesn't work, try to understand why and recursively go back to fill in the foundational details.
Example, download the code try to get it to work. Why is it not working? Oh it's trying to look for the model. Search for how to get the model and set it up. Then key step, recursively look up every single thing in the guide or set up. Don't try to set something up or fix some thing without truly understanding what it is you are doing (e.g. copy and paste). This gives you a structured why to fill in the foundations of what it is you are trying to get to work in a more focused and productive manner. At the end you might realize that their approach or yours is not optimal "oh it was telling me to download the 65k model when I can only run 7k on my machine bc ..."
After going through this series I can say I basically understand weights, tokens, back-propagation, layers, embeddings, etc.
Just curious, didn't see any date...
A model is some architecture of how data will flow through these weight matrices, along with the values of each weight.
Tokens are sort of "words" in a sentence, but the ML may be translating the word itself into a more abstract concept in 'word space': eg, a bunch of floating point values.
At least some of what I just said is probably wrong, but now someone will correct me and we'll both me more right!
> A model is some architecture of how data will flow through these weight matrices, along with the values of each weight.
Because data doesn't really flow through weight matrices, though perhaps this is true if you squint at very simple models. Deep learning architectures are generally more complicated than multiplying values by weights and pushing the results to the next layer, though which architecture to use depends heavily on context.
> Tokens are sort of "words" in a sentence
Tokens are funny. What a token is depends on the context of the model you're using, but generally a token is a portion of a word. (Why? Efficiency is one reason; handling unknown words is another.)
When doing quick estimates, I just assume every syllable is a token. It tends to overestimate, which is fine for my OOM mitigation purposes.