Transformer models are incredibly powerful for natural language tasks (and they are starting to find uses in many other fields of machine learning). Unfortunately, it is nigh-impossible to interpret what goes on inside them. OR IS IT???

In this post, I am trying to gauge potential community interest in a strand of research that I have been doing in my spare time off and on for the past year and a half (roughly).

I have found that I can, with a fair amount of effort, hard-code the weights of a transformer model in order to perform some very crude versions of linguistic tasks. So far I have achieved English-to-French translation (on a toy corpus of about 150 sentences), text classification (is a sentence grammatical or not? on a toy corpus of a couple hundred sentences), and sentiment analysis (again on a limited corpus). These results are obviously not impressive compared to the state of the machine learning field, but I am pretty sure that they can all be drastically scaled up with the investment of some time and energy. Unfortunately, I have a fairly demanding day job, and haven't found the time and energy yet.

All of this is done by inspection (no gradient descent!). The process is a lot like programming, although it is more difficult than programming, at least right now for me. I am fairly certain that better tools and better notation can be developed to make the process easier. It is also almost certainly possible to combine hard-coding with gradient descent approaches to be able to scale these methods up in a slightly less labor-intensive way.

I think that these ideas could prove useful in alignment research - if we understand how a language model works in excruciating detail, it seems drastically more likely that we will be able to reason about and predict various misunderstandings rooted in the ambiguity of language. Given that language is (arguably) a fully general means of interacting with an artificial intelligence, it seems plausible to me that this work is on the critical path to alignment.