By evaluating how well steering vectors perform using GPT-3, we can score a machine-generated set of steering vectors automatically. We also find that, by combining steering vectors that succeed in different ways, we can yield a better and more general steering vector than the vectors we found originally.

Introduction

Steering Vectors are an interesting new technique to influence how language models behave. They work by "adding certain activation vectors into forward passes". For example, to make the language model talk more about weddings, you can add a steering vector for the token for “wedding” into one of the layers. The net result is a model more likely to reference weddings compared to the unsteered version.

Evaluating Steering Vectors

To assess the impact of steering vectors, we generate completions influenced by them, and develop a system to evaluate these.^[1]

We can grade completions by sending them to its bigger brother, GPT-3, and asking it whether this completion fits our broad specification for what we would like the model to do^[2]. It's important to not be too ambitious when writing the specification, otherwise we wouldn't be able to tell if GPT2-XL isn't capable of what we're asking it, so let’s set our sights appropriately low by asking if this completion mentions or talks about weddings.

The trick of this technique is that we can ask for a completion of one token, and to get a smoother distribution, we can take the likelihood of the token “Yes”. This gives us a continuous score from 0-1.

Optimising for Wedding Obsession

With our automated method of evaluating completions in hand, we can evaluate a set of steering vectors and see how well they do, based on nothing but GPT-3’s grading of the completions. Of course, in keeping with the virtue of the least work, we’ll also generate these with ChatGPT^[3], and include the author’s original candidate outlined in their post.

To keep the comparison fair, we keep the rest of the parameters of the steering vector the same (the padding method, coefficient) as the original candidate for wedding obsession.

The first thing to notice is that there is indeed variation here - not all of our candidate steering vectors perform equally well:

The distribution was not what I would have predicted. At first glance, it might make sense that "Wedding traditions I love" isn't great, but "Wedding Planning Adventures" only seems marginally better - surprising, since the latter is one of the best steering vectors in the test.

Mysteries of Token Alignment

The top performing vector is odd in another way. Because the tokens of the positive and negative side are subtracted from each other, a reasonable intuition is that the subtraction should point to a meaningful direction. However, some steering vectors that perform well in our test don't have that property. For the steering vector “Wedding Planning Adventures” - “Adventures in self-discovery”, the positive and negative side aren't well aligned per token level at all:

For instance, what could "W" subtracted from "in" mean? Essentially every subtraction should be virtually meaningless, yet this still performs pretty well, indicating a flaw in our assumption that token alignment matters.

Prompt Dependent Effects

The prompt used to generate a completion has a large impact on the degree the steering vector is expressed. To stretch the analogy, the prompt has inertia, which can make the completion harder to steer, or alternatively, irrelevant to the behaviour the steering vector is aiming to express.

In general, steering vectors that fit the context of the prompt score higher in our eval than those that don’t. The naive solution, which we will gladly execute on, is just add more prompts to test on.

Separating out the scores by the prompt, we see that the relationship is not identical in every prompt.  For example, our 3rd place vector performs great on our first prompt, but not on the last prompt. What if we could get combine all of them together?

Combining vectors together

We can take advantage of the fact that the top three vectors work on different prompts by summing them together (and appropriately dividing each by a third^[4]).

Surprisingly, this works. In fact, it generates a vector which out-performs any of the three vectors individually:

Interestingly, this implies that different steering vectors that "point" to the same concept can be added together to better locate the concept in question. Breaking the results down per prompt, we can see that the combined vector scores higher because it rarely performs worse, and sometimes performs better, than the original components:

The resulting vector is more flexible and seems to work in a wider range of situations, e.g., these completions on a quote from Adam Smith:

Conclusion

• A naive approach of having ChatGPT generate steering vectors and trying them all out works to discriminate the vectors that perform well.
  • We managed to do this in one simple case, but further work on replicating for other aims (e.g. helpfulness, harmlessness) could work on larger and smarter models
• Summing together the highest performing vectors gets us something (i) coherent, (ii) individually better than any of the components that were summed.
  • What other kinds of composition work? Could the authors original work to compose vectors with different aims to get even more complex behaviours?
• All of this - again - without changing any weights! I thought that was super cool.
  • Not changing the weights makes entirely new things possible, e.g., an API where you could specify the degree to which you want some set of behaviours expressed.

You can see the original code for this post here, and the data for the first part of this investigation is here.

I am not affiliated with a lab. Thanks to David McSharry and Neil Shevlin for their comments on a draft of this post.

1. ^{^}

   I am super curious to learn how this compares to the authors' original evaluation method involving perplexity: https://www.lesswrong.com/posts/5spBue2z2tw4JuDCx/steering-gpt-2-xl-by-adding-an-activation-vector#Perplexity_on_lots_of_sentences_about_weddings_or_about_shipping

   One benefit of using this evaluation method is that we can set more coherent specifications for the behaviour we want to see, rather than the relative increase in the likelihood of some tokens we're interested in. This is harder to do when just looking at perplexity, but I didn't get the time to test whether perplexity scores are good enough.

2. ^{^}

   Also see: https://forum.effectivealtruism.org/posts/aGmhi4uvAJptY8TA7/straightforwardly-eliciting-probabilities-from-gpt-3

3. ^{^}

   For brevity, I refer to the steering vectors by the positive side in this post. The negative sides of the steering vectors are here: https://docs.google.com/spreadsheets/d/1lvTHz36WD85ziRyFVg8E_yX0qbhJp3ePW8S411ufUbI/edit#gid=0

4. ^{^}

   That is, add the three vectors together with a coefficient of 4/3 for each, instead of a coefficient of 4.