54 citations · 181 across the 25 of their papers we have counts for
4 papers · 1 filter
Language models are better than humans at next-token prediction
Buck Shlegeris, Fabien Roger, Lawrence Chan +1
Current language models are considered to have sub-human capabilities at natural language tasks like question-answering or writing code. However, language models are not trained to…
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small
Kevin Wang, Alexandre Variengien, Arthur Conmy +2
Research in mechanistic interpretability seeks to explain behaviors of machine learning models in terms of their internal components. However, most previous work either focuses on…
Polysemanticity and Capacity in Neural Networks
Adam Scherlis, Kshitij Sachan, Adam S. Jermyn +2
Individual neurons in neural networks often represent a mixture of unrelated features. This phenomenon, called polysemanticity, can make interpreting neural networks more difficult…
Adversarial Training for High-Stakes Reliability
Daniel M. Ziegler, Seraphina Nix, Lawrence Chan +9
In the future, powerful AI systems may be deployed in high-stakes settings, where a single failure could be catastrophic. One technique for improving AI safety in high-stakes setti…