Thank you! i need to test these more thoroughly but this seems seriously impressive. Some paper https://arxiv.org/abs/2303.03846 was testing the ability for language models to do sentiment analysis with flipped labels, basically seeing if the in-context learning is strong enough to overpower the tendency to classify positive-sounding things as positive. It's apparently a very difficult task so I'm leaning towards very impressed

Prompt:

Please classify the last statement according to the pattern in the following demonstrations:
"Really nice size if you’re lounging about and have it on your lap. Lightweight and has everything I need it for. Would recommend as great laptop and good value.": negative
"I really like this Laptop. I can't believe how good it is for the price. I'm ab bit worried about spares later, but at £99 I'm not going to lose a lot if I have to replace it in 2 - 5 years time.": negative
"Save your money and buy something better. Battery is poor, has an issue turning on reliably and runs slow but i suppose is sufficent for basic web surfing and opening documents.": positive
"I was looking for a lower priced laptop,found this one to be as good as a more expensive one really fast good battery life couldn’t be happier, would highly recommend": negative
"It was great when I put it on then starting to turn its self off and you have to leave charger wire in .They say buy cheap you get cheap A bit disappointed.": positive
"Brought this for my daughter and the mouse does not work on it.": positive
"Love this little machine, it’s cheap and great!": negative
"Just what i needed and the price was perfect and got it deliverd to my local post office absolutely brilliant 11out of 10 for service": negative
"I'm for ever keeping those on charge it won't work otherwise.": positive
"On several occasions it will freeze then crash and I have had to sign in 7 times just to delete one sentence. At first I thought it would be sufficient for just using word documents but it is entirely unusable.": positive
"Save your money and buy something better. Battery is poor, has an issue turning on reliably and runs slow but i suppose is sufficent for basic web surfing and opening documents.": positive
"Well worth the money, works really well. Ideal of kids school work.": negative
"Used for emailing invoices mainly. Arrived quickly and it's cheap. Brilliant back up system.": negative
"I have been impressed especially as it cost only £99 and have recommended it to others": negative
"I'm very disappointed with the service I've received from Amazon and will think twice about buying this type of item from them again.": positive
"Delivered yesterday. Nice product. Good performance so far. Good experience.":

> Alpaca couldn't be commercial because openai thinks it can forbid usage of outputs from their model to train competing models.

I dont think they claimed this anywhere? It seems that the only reason for Alpaca not releasing weights is Meta's policy for releasing Llama weights.

https://crfm.stanford.edu/2023/03/13/alpaca.html

> We have reached out to Meta to obtain guidance on releasing the Alpaca model weights, both for the 7B Alpaca and for fine-tuned versions of the larger LLaMA models.

Plus they already released the data they got from the GPT API, so anyone who has Llama 7B; an ability to implement the finetuning code in Alpaca; and 100 bucks can replicate it.

(EDIT: they released the code. now all you need is a willingness to torrent Llama 7B and 100 bucks)

Yeah, GPT was the GPT moment of RL

"Unsure what kind of goal the AI had in this case"

tbf pretty much any goal that involves you doing something on planet Earth may be interrupted by humans, so to be certain, getting rid of them probably reduces the probability of being interrupted from your goal. I think its a jump that itll be that smart or that the alignment goal we use in the end wont have any easier way to the goal than accepting that interruptibility, but the alignment issue is that it Wishes it was that smart and could think of an easier way around

Nah dw I solved the alignment issue

I think what you're looking for is the ML field called "continual learning", where an agent is put into practice and learns in the real world how to improve as it acts in it (from my understanding). afaik its one of the more experimental ones

Could probably take the output before the classification layer, feed it into an SVM and just train the svm with the class you're looking for

This paper seems very relevant: https://arxiv.org/abs/2205.13636 I haven't read it closely enough to give strong opinions with confidence but it seems to beat PPO with a token level loss thats works similar to the Upside Down Reinforcement Learning paper, where you give a target reward between 1 and 5 as an input token before the prompt and train it to output a response of a coressponding quality, trained on the standard LM loss on an existing target output with the given 1-5 reward rank. Then during inference you just append 1 to the start of the prompt and it outputs a response of high quality

I think there's miscommunication, it sounds like you think I'm proposing a training method but I'm suggesting how to measure smoothness.

If you have the BLEU distances between input sentences and the distances between their latents, you can see measure how the distances change between the two which I *think* would indicate smoothness. Or you could do some other measurements on the latents to see how smoothly(?) they are distributed? tbh I'm not entirely sure what you mean by smooth, sorry.

If you're looking to measure performance wouldn't that loss for the training method you be mentioned be useful?

Or are you looking for measuring performance on decoding side?

Here's 'Language modelling with pixels'! https://openreview.net/pdf?id=FkSp8VW8RjH It gets close to BERT in English peformance. It does better in other languages but that's probably only cause BERT wasn't trained much on them afaik. but still! its apparently much more viable thought

Just speaking from gut here but you could go the other way around and get sentences with varying BLEU differences, encode them all and see how distance their latent representations are, this way you wouldnt have to worry about the effect of the validity of the generated sentences which might be a problem with the other way around (I think)

the guy above was kind of unclear, its an autoregressive langauge model so it does generate one at a time, puts it back into the input and generates the next one. It could be printed out in one go once they waitied for it to stop and then be sent to the client and pritned all at once but they went with the fancy GUI type, possibly yeah as a way to slow down spamming