[R] Interpretability research in LLMs

Most work in interpretable ML for LLMs has focused on mechanistic interpretability, rather than previous approaches in the literature like counterfactuals, case-based reasoning, prototypes, saliency maps, concept-based explanation, etc...

Why do you think that is? My feeling is it's because mech interp is just less computationally intensive to research, so it's the only option people really have with LLMs (where e.g., datasets are too big to do case-based reasoning). The other explanation is that people are just trying to move …

case concept etc interpretability interpretability research literature llms machinelearning maps people reasoning research think work you