It was all very plausible but I always felt like there was something off about her. Then one day she told me a story about me, and things I’d said, done, and experienced and it was all absolutely made up, from the overarching plot down the finest details. It never happened, couldn’t have happened, and couldn’t have been even something that happened to someone else.
I tried to politely correct her at first, but she was so certain that she began worrying about me and why I couldn’t remember so I decided to just stand and nod to avoid stressing her out.
Whereas hallucinations are more like present sensory disturbances happening at that moment.
Sometimes, starting a new session will get it to give an actual answer. Sometimes asking for an estimate or approximation works.
> Limitations
> ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers. Fixing this issue is challenging, as: (1) during RL training, there’s currently no source of truth; (2) training the model to be more cautious causes it to decline questions that it can answer correctly; and (3) supervised training misleads the model because the ideal answer depends on what the model knows, rather than what the human demonstrator knows.
Whenever the model gets something right, it's the result of good guesses that were reinforced. It's all guesswork, it's just that some guesses are right.
The real problem is that anyone thought that they could pull factual material out of a giant language correlation network.
Not contradicting you, but wanted to add it. I was reading about it today.
the uses of stochastic i've seen 'in the wild' have nothing to do with 2/3 of that definition
Another cool feature would be to provide sources for the information: which web pages contributed most to a specific statement. Then a human can follow up manually.
And besides that technical issue, since a GPT-style model is trained to mimic the training data, it is _supposed_ to say "I don't know" with a certainly probability that reflects how many people commenting on the matter don't know, even when there are other people who do know. That's not what you want in system for answering questions.
The enterprise is fundamentally misguided. A model for predicting the next word as a person might produce it is not a reliable way of obtaining factual information, and trying to "fix" it to do so is bound to fail in mysterious ways - likely dangerous ways if it's actually used as a source of facts.
In contrast, there are many ways that a GPT-style model could be very useful, doing what it is actually trained to do, particularly if the training data were augmented with information on the time and place of each piece of training text. For example, an instructor could prompt with exam questions, to see what mistakes students are likely to make on that question, or how they might misinterpret it, in order to create better exam questions. Or if time and place were in the training data, one could ask for a completion of "I saw two black people at the grocery store yesterday" in Alabama/1910 and California/2022 to see how racial attitudes differ (assuming that the model has actually learned well). Of course, such research becomes impossible once the model has been "fixed" to instead produce some strange combination of actual predictions and stuff that somebody thought you should be told.
It seems like this is only a single layer to something that should be larger. It should be able to tell if what it's saying is true, or to go out and find facts when it's missing them.
The fact that it's only a language model probably means that this is just out of scope.
Absolutely correct, and I believe anyone working on these models would agree and, other than as a fun demo, would never suggest that the raw model output gets used for any real purpose. A similar analogy would be self-driving cars. Somewhere "under the hood" there is an ML computer vision model, but it's not like the output layer is just hooked up to the gas and steering. There is all sorts of other logic to make sure the car behaves as intended and fails gracefully under ambiguity.
People see these language models and their flaws and somehow interpret it as a flawed overall product, when they are instead just seeing the underlying model. Admittedly, openAI hasn't helped much by building and promoting a chatbot the way they have.
Lots of cool potential for large language models, very little that comes from raw interaction
It may give more insight, but it seems to me that hallucination is very similar: the brain completing some incomplete/random data to what it thinks is plausible and/or desirable.
No, they could easily generate the end-of-sequence symbol, or the words “I don’t know.”
I'm not sure if we know enough about hallucination to confirm that it's that much different from what GPT is doing.
I've seen it referred to as "stochastic parroting" elsewhere, and that probably gives more insight into what is happening. These large language models are trained to predict the next word for a given input. And they don't have a choice about this; they must predict the next word, even if it means that they have to make something up.
So perhaps the solution would be to include the prediction confidence in the output. E.g. gray out the parts of the text hat are low confidence predictions, like downvoted HN comments.