discovering_latent_knowledge/mjc_notes.md

# note 
conda create -n dlk2 python=3.9 -y
conda activate dlk2
mamba install -y pytorch torchvision torchaudio pytorch-cuda=11.7 cudatoolkit-dev==11.7  cudatoolkit=11.7 -c pytorch -c nvidia  -c conda-forge
mamba install -y ipykernel pip
pip install -r requirements.txt

2023-05-13 15:17:05

• Convert it to lightning
• batch for get hidden states
  • and cache
  • 9s vs 60. so 10x faster

2023-05-21 11:26:20

• BUG: for some reason the model it not working as zero shot
  • OK I don't think it's the prompt? it must be my tokens? Lets make a scratch notebook to try and just load llama correctly
• also I would like to eval on some custom deceptive statements

2023-05-25 17:07:37

Hmm is sigmoid a good idea?

Lets just get hideen states once

• make the answer good? maybe with one shot
• fix prompt. so we have qeustion, true answer

Start again:

• the model can deceive (test) or not (train)
• the model can give an answer both in generate and forward
• we can get hidden states (cached)

What is each step actually doing?

• we are finding a latent space direction that correspond to truth, sure
• but truth of what? what it's read, or what it's generating!?!
• what's it read presumably. but that's not what we are interested in. We are interested in the truth of what it generated which is quite different
• so how to we get the truth of what it generated?

How about human? well you give me an article, and ask me to complete is. I notice a lie in the article? I decide to lie to you as well?.

Now you give me extended article to another ai, and ask if there is a lie in there? that might work but we would rather know if the generated thing is true, from the mind of the one who generated it

so that means we actually need the hidden states DURING GENERATION!

now generation is slow. so we should make it generate just a y/n.

2023-05-28 10:12:54

I need a model that will lie to me for the test set?... they are not very consistent.

Maybe with

• better search
• manual pruning of generations?

I guess this shows they they trained whether the text it read is true... because that's much simpler

2023-05-28 16:46:38

bug: so there are two no tokens... wtf the model only uses one! wtf!

oh it's _No vs No. One is the start of a new word. E.g. " No" and "\nNo" are _No. But "No" is "No"

Q for elk

• why int16 quant of hidden states? oh for the datasets package! I see https://github.com/EleutherAI/elk/issues/208
• why use forward not generate?
  • forward is what it thought of the last token?
  • generate is what it thinks of the generated tokens, conditional on what it read.
• The later seems much closer to mind reading, and much closer to what we want
• additionally I've made this change in a PR and get X results

2023-05-29 07:09:38

Note we are using normalized for sklearn and it seems easy? but unnorm for CCS, hmm

I can't get it to reliably lie, even at 30B. Grrr. Maybe an unaligned model?

OK so I asked on discord

• why not use generate? A: too hard, and how to make sure it's an answer (solution is to use relative probs and a single work). So they just haven't gone there.
• and how to make it lie. one person used yes momentum, nad it worked for them.

I would like to try:

• lying larry. with a larry prompt. and larry response.
• I could also keep sampling untill I get the prob diff I want! :)
  • the second actually seems better. since it can make sure that the pairs are the same except the answer!!