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Can LLMs learn from a single example?

fast.ai

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Re: Can LLMs learn from a single example?

#81

Post by OhNoNotAgain_99 »

Yes it can Yesterday i gave it a help chapter in a prompt about angular 16 The knowledge cut of is perhaps nice for politics but not for programmers. After wards i could ask it about syntax problems i had in some code.

Essentially it understands programming didnt know what was possible in angular16 A single example made it learn from it. Though when i asked for an example i got the exact same sample as i had given it to learn from.

Perhaps end this language cut of for technical data. Its okay not wanting to get into politics (neither do i). But give it something to read (yup let it read and remember it) a simple prompt read this page by page will do, and give it some recent books, or popular coding websites, let it read python.org angular.io perhaps some modern manuals and books.

It also seemed keen to learn new information, it quickly adopted it. But only in that session.

Re: Can LLMs learn from a single example?

#82

Post by hashhar »

Nevermark wrote: post #70

Do people really use the phrase “over confident” in this way? It is very misleading. What is happening is called “over fitting”. Think of data as dots. A model that generalizes well will create as simple of a function as possible that fits the training data points pretty well. But keep training and parameters will often get very large, creating huge up and down swings in the function curve, far outside the actual dat…

This was an awesome explainer - thanks a lot. It helps clear up a lot of jargon I keep hearing in very precise ways.

Re: Can LLMs learn from a single example?

#83

Post by DougBTX »

mjburgess wrote: post #80
jph00 wrote: post #78

Earlier quoted context omitted.

No I don't use the term overfitting for a model where the accuracy is getting better. I think it's misleading.

Accuracy is very rarely a useful metric. It's more an engineering metric than something a user would ever care about. What users want is to have their own credences properly calibrated by engaging with some system. From a physics textbook, they want a systematic presentation of ideas which allows them to build intuitions etc. It's important to formulate the actual goal of the system, rather than just the engineer's g…

This is mixing two meanings of confidence which could lead to confusion. The OP is using confidence to describe how high the per-token probability scores are, while you are talking about the confidence expressed in the tone of voice of the language generated by the model. Really those are orthogonal issues. (Eg, a model could predict with high probability that a output should be “I don’t know”)

Re: Can LLMs learn from a single example?

#85

Post by alekseiprokopev »

minihat wrote: post #43
Yenrabbit wrote: post #5

Earlier quoted context omitted.

It does start getting worse at some point right?

Cross-entropy loss can start getting worse due to the model becoming less calibrated, even as the classification accuracy continues to improve. I first heard that here: https://arxiv.org/abs/1706.04599 Is this 'overconfidence' the leading explanation as to why LLMs continue to show qualitative improvement even after their test loss levels off?

Is it possible to somehow modify the sampling from the model to account for that?

Re: Can LLMs learn from a single example?

#86

Post by mirekrusin »

3abiton wrote: post #53
jph00 wrote: post #4

Earlier quoted context omitted.

I think our experiments actually don't show catastrophic forgetting! The accuracy does not decrease as loss gets worse -- it's simply getting over-confident. So I'm not even sure we're showing any problem to solve here -- it might be more of a opportunity, in fact!

Awesome investigative work, what's the opportunity though, I don't get it

It looks like something clicks in place.

Re: Can LLMs learn from a single example?

#88

Post by latexr »

tomaskafka wrote: post #84

Isn't this what people would do? I'd definitely update my knowledge after a single failed test question, if it was something I'd care about, and I discovered my previous model of reality was wrong.

> Isn't this what people would do?

It is not: https://en.wikipedia.org/wiki/Belief_perseverance

> I'd definitely update my knowledge after a single failed test question

Maybe you would, maybe you wouldn’t. There are several psychological experiments which show people don’t act the way they say they “definitely” would when confronted with the situation. Quite a few examples in the movie “Experimenter”: https://en.wikipedia.org/wiki/Experimenter_(film)

> if it was something I'd care about, and I discovered my previous model of reality was wrong.

Those two ifs are doing a ton of heavy lifting. LLMs neither “care” nor “discover”. It’s not like you’re giving it a new contradicting piece of information and it’s going “interesting, let me research on that and update my model of reality if after careful consideration I find your assertion to be true”. It’s closer to having someone who’ll just accept everything you say and repeat it.

Re: Can LLMs learn from a single example?

#89

Post by theonlybutlet »

famouswaffles wrote: post #3
jph00 wrote: post #2

Thank you for posting this to HN! :D I'm one of the authors of this post -- Johno & I found it really interesting looking into this curious issue of rapid memorization from LLMs. I've been working with neural nets for 30 years, and fine-tuning language models since 2017, and this behavior is most surprising to me! Other folks have seen it in LLMs too, although I haven't seen a analysis of this kind before (although w…

In the palm-e paper ( https://palm-e.github.io/ ), when they try to unfreeze and train the LLM on new image data only, there is expectedly a lot of CF on NLP tasks but very interestingly, the effect diminishes greatly with the scale of the LLM prior to training. From an average -87.3% performance drop on the 12B model to -61.6% on the 84B model then just -3.9% on the 562B model. Felt like we were just shy of an insig…

What does CF stand for?

Re: Can LLMs learn from a single example?

#90

Post by miven »

jacquesm wrote: post #75
bathtub365 wrote: post #52

Earlier quoted context omitted.

I’ve found the opposite in trying to get it to play Wordle. It’ll repeatedly forget things it’s seemingly learned within the same session, all the while confident in its correctness.

LLMs are trained on 'tokens' derived from 'words' and 'text' and even though there are tokens that are just one letter the bulk is a rough approximation to syllables as though you're trying to create a dictionary to be used for data compression. It might be more effective to try to play 'tokendle' before trying to play 'wordle'.

Do you know whether LLMs grasp the equivalence of a word expressed as one whole-word token and as a series of single character tokens that spell out the same word? I'm curious if modifying the way some input words are split into tokens could be useful for letter-by-letter reasoning like in Wordle.

Or would an LLM get confused if we were to alter the way the tokenization of the input text is done, since it probably never encountered other token-"spellings" of the same word?

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