[HarHarVeryFunny]

> It was trained not only to "sound good" aesthetically but also to solve a wide range of NLP tasks accurately.

Was it? I've only heard of pre-training (predict next word) and subsequent RLHF + SFT "alignment" (incl. aligning to goal of being conversational). AFAIK the NLP skills that these LLMs achieve are all emergent rather than explicitly trained.

I'm not sure we can really say the net fully understands even if it answers as if it does - it was only trained to "predict next word", which in effect means being trained to generate a human-like response. It will have learnt enough to accomplish that goal, and no more (training loss tends to zero as goal is met).

Contrast this to an animal with a much richer type of feedback - reality, and with continual (aka online) learning. The animal truly understands it's actions - i.e. has learnt to accurately predict what will happen as a result of them.

The LLM does not understand it's own output in this sense - it exists only in a world of words, and has no idea if the ideas it is expressing are true or not (hence all the hallucinating/bullshitting). It only knew enough to generate something that sounded like what a person might say.

[xcv123]

> Was it? I've only heard of pre-training (predict next word) and subsequent RLHF + SFT "alignment" (incl. aligning to goal of being conversational). AFAIK the NLP skills that these LLMs achieve are all emergent rather than explicitly trained.

I believe you are right about that. I did some research after reading your comment. Transformers were certainly designed for NLP, but with large enough models the abilities can emerge without necessarily being explicitly trained for it.

> I'm not sure we can really say the net fully understands even if it answers as if it does - it was only trained to "predict next word", which in effect means being trained to generate a human-like response.

It depends on your definition of "understand". If that requires consciousness then there is no universally agreed formal definition.

Natural Language Understanding (NLU) is a subset of Natural Language Processing (NLP). If we take the word "understanding" as used in an academic and technical context then yes they do understand quite well. In order to simply "predict the next word" they learn an abstract model of syntax, semantics, meaning, relationships, etc, from the text.

> and has no idea if the ideas it is expressing are true or not (hence all the hallucinating/bullshitting).

That is not really an issue when solving tasks that are within it's context window. It is an issue for factual recall. The model is not a type of database that stores its training set verbatim. Humans have analogous problems with long term memory recall. I can think straight within my working memory but my brain will "hallucinate" to some extent when recalling distant memories.

[HarHarVeryFunny]

The context window only has to do with the size of input it has access to - its not related to what it's outputting, which is ultimately constrained by what it was trained on.

If you ask it a question where the training data (or input data = context) either didn't include the answer, or where it was not obvious how to get the right answer, that will not (unfortunately) stop it from confidently answering!

[xcv123]

> The context window only has to do with the size of input it has access to - its not related to what it's outputting, which is ultimately constrained by what it was trained on.

Wait a minute. You are completely missing the entire "attention mechanism" thing which is what makes transformers so capable. For each output token generated in sequence, the attention mechanism evaluates the current tokens relationship to all tokens in the context window, weighing their relevance. There are multiple "attention heads" running in parallel (16 in GPT-3.5). Now for each layer of the neural network there is an attention mechanism, independently processing the entire context window for each token. There are ~100 layers in ChatGPT. So now we have 100 layers times 16 attention heads = 1600 attention mechanisms evaluating the entire context window over many deep layers of abstraction for each output token.

[HarHarVeryFunny]

I'm not sure what your point is ... Hallucinations are where the net hadn't seen enough training data similar/related to the prompt to enable it to generate a good continuation/response. Of course in cases where it is sufficiently trained and the context contained what it needs then in can make full use of it, even copying context words to the output (zero shot learning) when appropriate.

The real issue isn't that the net often "makes a statistical guess" rather than saying "I don't know", but rather that when it does make errors it has no way to self-detect the error and learn from the mistake, as a closed-loop biological system is able to do.

[xcv123]

I was responding to this.

> The context window only has to do with the size of input it has access to - its not related to what it's outputting

The sequential token generation process is closely related to the content of the context window at every step.

Maybe I misunderstood your point. I know these things can hallucinate when asked about obscure facts that they weren't sufficiently trained on.

[xcv123]

> If you ask it a question where the training data (or input data = context) either didn't include the answer, or where it was not obvious how to get the right answer, that will not (unfortunately) stop it from confidently answering!

I haven't found this to be the case in my experience. I use ChatGPT-4. It often tells me when it doesn't know or have enough information.

If you haven't used GPT-4 I recommend signing up for a month. It is next level, way better than 3.5. (10x the parameter count). (No I'm not being paid to recommend it.)