42 citations · 76 across the 13 of their papers we have counts for
6 papers · 1 filter
Multilevel Interpretability Of Artificial Neural Networks: Leveraging Framework And Methods From Neuroscience
Zhonghao He, Jascha Achterberg, Katie Collins +13
As deep learning systems are scaled up to many billions of parameters, relating their internal structure to external behaviors becomes very challenging. Although daunting, this pro…
The Remarkable Robustness of LLMs: Stages of Inference?
Vedang Lad, Jin Hwa Lee, Wes Gurnee +1
We investigate the robustness of Large Language Models (LLMs) to structural interventions by deleting and swapping adjacent layers during inference. Surprisingly, models retain 72-…
Confidence Regulation Neurons in Language Models
Alessandro Stolfo, Ben Wu, Wes Gurnee +4
Despite their widespread use, the mechanisms by which large language models (LLMs) represent and regulate uncertainty in next-token predictions remain largely unexplored. This stud…
Refusal in Language Models Is Mediated by a Single Direction
Andy Arditi, Oscar Obeso, Aaquib Syed +4
Conversational large language models are fine-tuned for both instruction-following and safety, resulting in models that obey benign requests but refuse harmful ones. While this ref…
Not All Language Model Features Are One-Dimensionally Linear
Joshua Engels, Eric J. Michaud, Isaac Liao +2
Recent work has proposed that language models perform computation by manipulating one-dimensional representations of concepts ("features") in activation space. In contrast, we expl…
Universal Neurons in GPT2 Language Models
Wes Gurnee, Theo Horsley, Zifan Carl Guo +5
A basic question within the emerging field of mechanistic interpretability is the degree to which neural networks learn the same underlying mechanisms. In other words, are neural m…