I am contrasting generating an output by:

1. Modeling how a human would respond (“human modeling in output generation”)
2. Modeling what the ground-truth answer is

Eg. for common misconceptions, maybe most humans would hold a certain misconception (like that South America is west of Florida), but we want the LLM to realize that we want it to actually say how things are (given it likely does represent this fact somewhere)

This is really cool work!!

In other experiments we've run (not presented here), the MSP is not well-represented in the final layer but is instead spread out amongst earlier layers. We think this occurs because in general there are groups of belief states that are degenerate in the sense that they have the same next-token distribution. In that case, the formalism presented in this post says that even though the distinction between those states must be represented in the transformers internal, the transformer is able to lose those distinctions for the purpose of predicting the next token (in the local sense), which occurs most directly right before the unembedding.

Would be interested to see analyses where you show how an MSP is spread out amongst earlier layers.

Presumably, if the model does not discard intermediate results, something like concatenating residual stream vectors from different layers and then linearly correlating with the ground truth belief-state-over-HMM-states vector extracts the same kind of structure you see when looking at the final layer. Maybe even with the same model you analyze, the structure will be crisper if you project the full concatenated-over-layers resid stream, if there is noise in the final layer and the same features are represented more cleanly in earlier layers?

In cases where redundant information is discarded at some point, this is a harder problem of course.

Profound!

It's possible that once my iron reserves were replenished through supplementation, the amount of iron needed to maintain adequate levels was lower, allowing me to maintain my iron status through diet alone. Iron is stored in the body in various forms, primarily in ferritin, and when levels are low, the body draws upon these reserves.

I'll never know for sure, but the initial depletion of my iron reserves could have been due to a chest infection I had around that time (as infections can lead to decreased iron absorption and increased iron loss) or a period of unusually poor diet. 

Once my iron reserves were replenished, my regular diet seemed to be sufficient to prevent a recurrence of iron deficiency, as the daily iron requirement for maintenance is lower than the amount needed to correct a deficiency. 

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.