Nicky Pochinkov
NickyP
Karma: 756
Linear vs Non-linear Probes for Interpretability
I respond to you in my next post but i’m also copying here:
I think this is one case where language is a bit inadequate.
I think it is very possible for “suffering” to be good. There are two cases for this:
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“suffering” in which states are described as negative, but which are still positive valence. One example of this is the burn one feels from spicy food. This still feels good and is pleasurable, despite nominally having aspects which are described as bad. Some similar things are when crying feels cathartic. Or people who gain direct pleasure from painful stimuli. Often there is a limit to how far one can go before the direct pain stops feeling directly pleasurable, but there is a lot of variation in the human mind, and some people gain mental pleasure from being able to withstand levels of pain that are considered unbearable. This can be due to to things like feelings of pride, or servitude, or novelty.
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“suffering” in which one was actually in pain/suffering at the time of the even, but which leads one to better mental states after the fact. Perhaps it leads one to grow and fix one’s other problems. Perhaps it is a memorable experience one finds valuable.
I have experienced both. Suffering can be a way to describe this, if the experience is also either positive-valence, or leading to longer-term pleasure, then I’m not sure it counts.
I think there are some forms of suffering that are near universally felt as bad. This can be chronic pain one gets from illness, or the suffering one can feel when feverish, scenarios of starvation or hunger, or through effective torture. And I guess with “suffering is bad” I am trying to point more-so at this.
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Is death and suffering axiomatically bad?
My Ethics
I’ve tried swipe for maybe like an hour or two but didn’t like it that much. It’s been a whike though and maybe i’m missing out.
How much faster is speaking, compared to typing on laptop vs phone vs writing?
Two Theories for Cryopreservation
carbon offset arbitrage opportunity
Dying with Whimsy
I’m also going to note tts podcast feeds exist:
https://feeds.type3.audio/lesswrong--30-karma.rss
and
Modelling Trajectories—Interim results
Hmm, it seems the when I achieved the virtue of The Void, it was absorbed by the void
Yeah, the context length was 128 concepts for the small tests they did between architectures, and 2048 concepts for the larger models.
How this exactly translates is kind of variable. They limit the concepts to be around 200 characters, but this could be any number of tokens. They say they trained the large model on 2.7T tokens and 142B concepts, so on average 19 tokens per concept.
The 128 would translate to 2.4k tokens, and the 2048 concepts would translate to approx 39k tokens.
Yeah it was annoying to get working. I now have added a Google Colab in case anyone else wants to try anything.
It does seem interesting that the semantic arithmetic is hit or miss (mostly miss).
Energy Markets Temporal Arbitrage with Batteries
Distillation of Meta’s Large Concept Models Paper
Thanks for reading, and yeah I was also surprised by how well it does. It does seem like there is degradation in auto-encoding from the translation, but I would guess that it probably does also make the embedding space have some nicer properties
I bet if you add Gaussian noise to them they still decode fine
I did try some small tests to see how sensitive the Sonar model is to noise, and it seems OK. I tried adding gaussian noise and it started breaking at around >0.5x the original vector size, or at around cosine similarity <0.9, but haven’t tested too deeply, and it seemed to depend a lot on the text.
There also appears to be a way to attempt to use this to enhance model capabilities
I meta’s newer “Large Concept Model” paper they do seem to manage to train a model solely on Sonar vectors for training, though I think they also fine-tune the Sonar model to get better results (here is a draft distillation I did. EDIT: decided to post it). It seems to have some benefits (processing long contexts becomes much easier), though they don’t test on many normal benchmarks, and it doesn’t seem much better than LLMs on those.
The SemFormers paper linked I think also tries to do some kind of “explicit planning” with a text auto-encoder but I haven’t read it too deeply yet. I briefly gleamed that it seemed to get better at graph traversal or something.
There are probably other things people will try, hopefully some that help make models more interpretable.
can we extract semantic information from this 1024-dimensional embedding vector in any way substantially more efficient than actually decoding it and reading the output?
Yeah I would like for there to be a good way of doing this in the general case. So far I haven’t come up with any amazing ideas that are not variations on “train a classifier probe”. I guess if you have a sufficiently good classifier probe setup you might be fine, but it doesn’t feel to me like something that works in the general case. I think there is a lot of room for people to try things though.
I wonder how much information there is in those 1024-dimensional embedding vectors… [Is there] a natural way to encode more tokens
I don’t think there is any explicit reason to limit to 512 tokens, but I guess it depends how much “detail” needs to be stored. In the Large Concept Models paper, the experiments on text segmentation did seem to degrade after around ~250 characters in length, but they only test n-gram BLEU scores.
I also guess that if you had a reinforcement loop setup like in the vec2text inversion paper, that you could probably do a good job getting even more accurate reconstructions from the model.
Exploring this embedding space seems super interesting
Yeah I agree, while it is probably imperfect, I think it seems like an interesting basis.
Ok thanks, not sure why that happened but it should be fixed now.
I just wrote this in my next post but TLDR I think even beyond this, I have some preference for: current world continue to exist VS current world ends and is replaced with one with a similar amount of pleasure/suffering. It’s probably me just being biased, but I do still hold this somewhat.