Wiki Contributions



Hi Charlie, yep it's in the paper - but I should say that we did not find a working CUDA-compatible version and used the scikit version you mention. This meant that the data volumes used are somewhat limited - still on the order of a million examples but 10-50x less than went into the autoencoders.

It's not clear whether the extra data would provide much signal since it can't learn an overcomplete basis and so has no way of learning rare features but it might be able to outperform our ICA baseline presented here, so if you wanted to give someone a project of making that available, I'd be interested to see it!

seems like it'd be better formatted as a nested list given the volume of text

Why would we expect the expected level of danger from a model of a certain size to rise as the set of potential solutions grows?

I think both Leap Labs and Apollo Research (both fairly new orgs) are trying to position themselves as offering model auditing services in the way you suggest.

A useful model for why it's both appealing and difficult to say 'Doomers and Realists are both against dangerous AI and for safety - let's work together!'.

Try decomposing the residual stream activations over a batch of inputs somehow (e.g. PCA). Using the principal directions as activation addition directions, do they seem to capture something meaningful?

It's not PCA but we've been using sparse coding to find important directions in activation space (see original sparse coding post, quantitative results, qualitative results).

We've found that they're on average more interpretable than neurons and I understand that @Logan Riggs and Julie Steele have found some effect using them as directions for activation patching, e.g. using a "this direction activates on curse words" direction to make text more aggressive. If people are interested in exploring this further let me know, say hi at our EleutherAI channel or check out the repo :)

Hi, nice work! You mentioned the possibility of neurons being the wrong unit. I think that this is the case and that our current best guess for the right unit is directions in the output space, ie linear combinations of neurons.

We've done some work using dictionary learning to find these directions (see original post, recent results) and find that with sparse coding we can find dictionaries of features that are more interpretable the neuron basis (though they don't explain 100% of the variance). 

We'd be really interested to see how this compares to neurons in a test like this and could get a sparse-coded breakdown of gpt2-small layer 6 if you're interested.

Link at the top doesn't work for me

I still don't quite see the connection - if it turns out that LLFC holds between different fine-tuned models to some degree, how will this help us interpolate between different simulacra?

Is the idea that we could fine-tune models to only instantiate certain kinds of behaviour and then use LLFC to interpolate between (and maybe even extrapolate between?) different kinds of behaviour?

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