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The wrapper modules simply wrap existing submodules of the model, and call whatever they are wrapping (in this case self.attn) with the same arguments, and then save some state / do some manipulation of the output. It's just the syntax I chose to use to be able to both save state from submodules, and manipulate the values of some intermediate state. If you want to see exactly how that submodule is being called, you can look at the llama huggingface source code. In the code you gave, I am adding some vector to the hidden_states returned by that attention submodule. 

We used the same steering vectors, derived from the non fine-tuned model

I think another oversight here was not using the system prompt for this. We used a constant system prompt of “You are a helpful, honest and concise assistant” across all experiments, and in hindsight I think this made the results stranger by using “honesty” in the prompt by default all the time. Instead we could vary this instruction for the comparison to prompting case, and have it empty otherwise. Something I would change in future replications I do.

Yes, this is almost correct. The test task had the A/B question followed by My answer is ( after the end instruction token, and the steering vector was added to every token position after the end instruction token, so to all of My answer is (.

Yes, this is a fair criticism. The prompts were not optimized for reducing or increasing sycophancy and were instead written to just display the behavior in question, like an arbitrarily chosen one-shot prompt from the target distribution (prompts used are here). I think the results here would be more interpretable if the prompts were more carefully chosen, I should re-run this with better prompts.

Is my understanding correct or am I missing something?

  • A latent variable is a variable you can sample that gives you some subset of the mutual information between all the different X's + possibly some independent extra "noise" unrelated to the X's
  • A natural latent is a variable that you can sample that at the limit of sampling will give you all the mutual information between the X's - nothing more or less

E.g. in the biased die example, every each die roll sample has, in expectation, the same information content, which is the die bias + random noise, and so the mutual info of n rolls is the die bias itself

(where "different X's" above can be thought of as different (or the same) distributions over the observation space of X corresponding to the different sampling instances, perhaps a non-standard framing)

suppose we have a 500,000-degree polynomial, and that we fit this to 50,000 data points. In this case, we have 450,000 degrees of freedom, and we should by default expect to end up with a function which generalises very poorly. But when we train a neural network with 500,000 parameters on 50,000 MNIST images, we end up with a neural network that generalises well. Moreover, adding more parameters to the neural network will typically make generalisation better, whereas adding more parameters to the polynomial is likely to make generalisation worse. 

 

Only tangentially related, but your intuition about polynomial regression is not quite correct. A large range of polynomial regression learning tasks will display double descent where adding more and more higher degree polynomials consistently improves loss past the interpolation threshold. 

Examples from here:

Relevant paper

It does seem like small initialisation is a regularisation of a sort, but it seems pretty hard to imagine how it might first allow a memorising solution to be fully learned, and then a generalising solution.

 

"Memorization" is more parallelizable and incrementally learnable than learning generalizing solutions and can occur in an orthogonal subspace of the parameter space to the generalizing solution. 

And so one handwavy model I have of this is a low parameter norm initializes the model closer to the generalizing solution than otherwise, and so a higher proportion of the full parameter space is used for generalizing solutions. 

The actual training dynamics here would be the model first memorizes a high proportion of the training data while simultaneously learning a lossy/inaccurate version of the generalizing solution in another subspace (the "prioritization" / "how many dimensions are being used" extent of the memorization being affected by the initialization norm). Then, later in training, the generalization can "win out" (due to greater stability / higher performance / other regularization). 

Ah, yes, good spot. I meant to do this but somehow missed it. Have replaced the plots with normalized PCA. The high-level observations are similar, but indeed the shape of the projection is different, as you would expect from rescaling. Thanks for raising!

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