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.

3mo95

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

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