Different attribution methods can be placed on a scale, with the X-axis being the reflection of grouth truth (at least for the interpretation of the image task, reflecting how humans process information) and the Y-axis being how the model processes information in its way. Attribution methods can highlight most truths, but do not necessarily accurately reflect how the model learns things. The attribution method is a representation of the model, and the model is a representation of the data. Different levels of accuracy imply different levels of uncertainty associated with the model's predictions, which implies inherent uncertainty in the attribution methods. Perhaps it would be good to understand how humans perceive uncertainty and interpretations of models (positioned on the scale the good balance of representing truth and representing models) before designing a new "better" approach. For more complex task, there may not be grouth truth tho.
I have been wondering if neural networks (or more specifically, transformers) will become the ultimate form of AGI. If not, will the existing research on Interpretability, become obsolete?
I am trying to look into the code here
But the links dont work anymore! It would be nice if you could help update them!
I dont know if this link works for the original content: https://colab.research.google.com/github/neelnanda-io/Easy-Transformer/blob/clean-transformer-demo/Clean_Transformer_Demo_Template.ipynb
Thanks a lot!
So far the best summary I have seen!