TL;DR: By just adding e.g. a "sycophancy vector" to one bias term, we outperform supervised finetuning and few-shot prompting at steering completions to be more or less sycophantic. Furthermore, these techniques are complementary: we show evidence that we can get all three benefits at once!
Summary: By adding e.g. a sycophancy vector to one of the model's bias terms, we make Llama-2-{7B, 13B}-chat more sycophantic. We find the following vectors:
These vectors are[1] highly effective, as rated by Claude 2:
We find that the technique generalizes better than finetuning while only slightly decreasing... (read 2172 more words →)
I think this is a very good critique of OpenAI's plan. However, to steelman the plan, I think you could argue that advanced language models will be sufficiently "generally intelligent" that they won't need very specialized feedback in order to produce high quality alignment research. As e. g. Nate Soares has pointed out repeatedly, the case of humans suggests that in some cases, a system's capabilities can generalize way past the kinds of problems that it was explicitly trained to do. If we assume that sufficiently powerful language models will therefore have, in some sense, the capabilities to do alignment research, the question then becomes how easy it will be for us... (read more)