91–100 of 257 posts
Earlier quoted context omitted.
Calling them hallucinations was a huge mistake.
I won't defend the term but am curious what you think would have been also concise but more accurate. Calling them for example "inevitable statistical misdirections" doesn't really roll off the tongue.
MetaAI makes up stuff reliably. You'd think it would be an ace at baseball stats for example, but "what teams did so-and-so play for", you absolutely must check the results yourself.
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"It" does not know when it does not know. But it does know when it has uncertainty. In the chatgpt api this is logprobs, each generated token has a level of uncertainty, so: "2+2=" The next token is with almost 100% certainty 4. "Today I am feeling" The next token will be very uncertain, it might be "happy", it might be "sad", it might be all sorts of things.
"The next token is with almost 100% certainty 4." By using the word "almost" with regards 2 + 2 = 4, you have not exactly dispelled LLM "nonsense". A human (with a modicum of maths knowledge) will know that 2 + 2 = 4 (pure integers - a fact by assertion). A maths worrier will get slightly uncomfortable about 2.0 + 2.0 = 4.0 unless they are ensured that decimal places and accuracy are the same thing and a few other th…
Also, the next token might be 2 and the next token might be ², the next token could also have been x, these are all valid statements and the LLM might have been uncertain because of them.
2+2=4
2+2=x
2+2=2x
2+2=2x2
2+2=2²
Are all valid statements.
Earlier quoted context omitted.
How are you defining hallucination then? In some pretty useless way inevitably. Hallucinations are precisely the generated expressions that don't correlate with reality or are not truthful.
I don't think that definition works: it's attempting to categorize statements according to criteria completely external to them rather than according to any inherent property of the statement. A better definition is that a hallucination is an expression that is generated within a closed system without direct input from the reality it is meant to represent. The point is that an expression about reality that doesn't co…
Correct. The basic concept of truth in logic relies on an objective reality, an expression a priori holds truth even in the absence or indistinct of such a reality. But the truthfulness or correctness of a posteriori statements can depend on the reality. Examples of the former would be "If A is B, then B is C. A is B, then B is C" Example of the latter would be "It is raining outside."
"A better definition is that a hallucination is an expression that is generated within a closed system without direct input from the reality it is meant to represent. The point is that an expression about reality that doesn't come from observing reality can only be true coincidentally."
Absolutely incorrect, you are talking about a concept of the state of the art of science and tech but you are failing basic philosophy and epistemology concepts. The LLM has inputs from the reality (is it possible not to?), it is trained on a huge corpus of text written by humans that themselves perceive reality. The perception of reality can be indirect. We can measure something by observing it, or by observing an instrument that in turn observes it.
"but the dream an the LLM alike are effectively just "remixing" prior input, and not generating any new observations of reality."
Again incorrect for three reasons:
1- Novel observations can occur purely from remixing. Einstein locked himself during a pandemic and developed the theory of relativity without additional experimental output.
2- LLMs combine their existing data with human input, which is an external source.
3- LLMs can interact with other sources of data whether by injection of data into the prompt, by function calling, RAG, etc..
So yeah. Try to go back to basics and study simpler systems, ideally with source code. This might be out of your league.
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I disagree with this take, Stallman has expressed it recently by linking some "scientific article". While I get that LLMs generate text in some way that does not guarantee correctness. There is a correlation between generated text and correctness, which is why millions of people use it... You can judge the correctness of a sentence generated by an LLM. In the same way you can judge the correctness of a human generate…
> I disagree with this take, Stallman has expressed it recently by linking some "scientific article". I don't know how to parse this. What article did Stallman "link", and what are you saying Stallman "expressed" by linking/using it? > whether the truthness or correlation with reality of an LLM sentence can be judged on its own or whether it requires a human to interpret it is not very relevant It's incredibly releva…
Earlier quoted context omitted.
"The next token is with almost 100% certainty 4." By using the word "almost" with regards 2 + 2 = 4, you have not exactly dispelled LLM "nonsense". A human (with a modicum of maths knowledge) will know that 2 + 2 = 4 (pure integers - a fact by assertion). A maths worrier will get slightly uncomfortable about 2.0 + 2.0 = 4.0 unless they are ensured that decimal places and accuracy are the same thing and a few other th…
You might be in the wrong thread. This is merely a comment about whether LLMs hold a concept of uncertainty, they do. Also, the next token might be 2 and the next token might be ², the next token could also have been x, these are all valid statements and the LLM might have been uncertain because of them. 2+2=4 2+2=x 2+2=2x 2+2=2x2 2+2=2² Are all valid statements.
Earlier quoted context omitted.
You might be in the wrong thread. This is merely a comment about whether LLMs hold a concept of uncertainty, they do. Also, the next token might be 2 and the next token might be ², the next token could also have been x, these are all valid statements and the LLM might have been uncertain because of them. 2+2=4 2+2=x 2+2=2x 2+2=2x2 2+2=2² Are all valid statements.
And somewhere in its training data, you can be sure there's also 2+2=5.
The idea that the logprobs of any token is proportional to the amount of times it comes up in training data is not true.
For example, suppose that A is a common misconception and is repeated often in Reddit, but B appears in scholarly textbooks and papers, and higher reputation data sources. Then through reinforcement the logprobs of B can increase, and they can increase consistently when surrounded by contexts like "This is true" and conversely decrease in contexts of "this is not true".
So the presumptions and values of its trainers are also embedded into the LLM in addition to those of the authors of the text corpus.
To my understanding, the reason why companies don't mind the hallucinations is the acceptable error rate for a given system. Let's say something hallucinated 25% of the time, but if that's ok, then it's fine for a certain product. If it only hallucinates 5% of the time, it's good enough for even more products and so on. The market will just choose the LLM appropriately depended on the tolerable error rate.
At scale, you are doing the same thing with humans too. LLMs seem to have an error rate similar to humans for the majority of simple, boring tasks, if not even a bit better since they don't get distracted and start copying and pasting their previous answers. The difference with LLMs is they simply cannot (currently) do the most complex tasks that some humans can, and when they do produce erroneous output, the errors…
This is the big one missed by the frequent comments on here wondering whether LLMs are a fad, or claiming in their current state they cannot be used to replace humans in non-trivial real-world business workflows. In fact, even 1.5 years ago at the time of GPT 3.5, the technology was already good enough.
The yardstick is the peformance of humans in the real world on a specific task. Humans, often tired, having a cold, distracted, going through a divorce. Humans who even when in a great condition make plenty of mistakes.
I guess a lot of developers struggle with understanding this because so far when software has replaced humans, it was software that on the face of it (though often not in practice) did not make mistakes if bug-free. But that has been never been necessary for software to replace humans - hence buggy software still succeeding in doing so. Of course, often software even replaces humans when it's worse at a task for cost reasons.
They're at the very least competitive, if not better than, doctors at diagnosing illnesses [1].
[1] https://www.nytimes.com/2024/11/17/health/chatgpt-ai-doctors...