You won't catch everything but I've found if you specify that it's okay to respond that it doesn't know the answer and essentially accuracy on a smaller subset of answers > responding to all, it often will say it doesn't know rather than making something up.

Another option could be - assuming this is an LLMChain that adds the most similar (n) embedding(s) to the prompt before passing to LLM - basic entity recognition to find the asset name from the query initially and dynamic prompt that if the most similar results don't contain the asset name, don't provide examples and respond saying you don't know.

So no then

(I'm going to use the word 'word' instead of 'token' because that's more intuitive to understand)

I can't speak for this model specifically, but the way GPT works is by first coming up with a list of likely words along with their probabilities of being the next word to follow from the current state of the prompt. Then it uses some math (Top-P controls how much of that list to use, temperature controls how strongly it should prefer the 'most likely' words as opposed to the lesser likely ones) to randomly select the next word. Repeat until you're happy with the length.

Visual example: https://i.imgur.com/FvoSiC7.png

In this way, the model knows how likely the word it's about to choose is, but that says nothing about whether it's hallucinating or not. All the words in the list are equally 'true' to the model.

So, no. Not currently. All LLM's know is how likely a word is to appear next to another word, given the preceding X,000 words.

I feel like "hallucinating" is the wrong word. It is essentially predicting the next word based upon its neural net and training. If this training does not have the right data, it will predict things that are wrong.

While knowing might be impossible, it seems like the model could provide a confidence level and only provide answers that exceed some threshold. It'd be a bit like asking a human "are you sure about that?"

And in practice, I really don't think it is that different. We humans effectively make things up all the time. Sometimes we are well aware of our educated guesses and sometimes we are less aware.

It isn't realistic to expect an artificial intelligence to be vastly better than human intelligence in this regard.

The catch is that the models don't have confidence. They cannot distinguish between knowing something for sure, guessing something and hallucinating false knowledge.

Perhaps researchers will find some clever solution to mitigate this, but for now hallucinating is a pretty good word precisely because the model doesn't give confidence.

The Internal State of an LLM Knows When its Lying

https://arxiv.org/abs/2304.13734

It's not surprising that this information is somehow saved in the internal state, but it is surprising that they were able to read it to some level.

I don't think the model can access this information, but an external "lie detector" would be interesting. Thanks for the paper.

that seems wrong.. don't llms runs on probability distributions? Won't they have confidence built in?

I don't think probability of tokens can be mistaken for confidence of knowledge being correct. I'd take it more as a fitness whether the tokens fit naturally in the sentence.

Makes sense, but i wonder if there's some way to retrofit that functionality onto it