research
∙
07/18/2023
Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla
Circuit analysis is a promising technique for understanding the internal...
share
research
∙
05/31/2023
Neuron to Graph: Interpreting Language Model Neurons at Scale
Advances in Large Language Models (LLMs) have led to remarkable capabili...
research
∙
05/02/2023
Finding Neurons in a Haystack: Case Studies with Sparse Probing
Despite rapid adoption and deployment of large language models (LLMs), t...
research
∙
04/22/2023
N2G: A Scalable Approach for Quantifying Interpretable Neuron Representations in Large Language Models
Understanding the function of individual neurons within language models ...
research
∙
02/06/2023
A Toy Model of Universality: Reverse Engineering How Networks Learn Group Operations
Universality is a key hypothesis in mechanistic interpretability – that ...
research
∙
01/12/2023
Progress measures for grokking via mechanistic interpretability
Neural networks often exhibit emergent behavior, where qualitatively new...
research
∙
09/24/2022
In-context Learning and Induction Heads
"Induction heads" are attention heads that implement a simple algorithm ...
research
∙
04/12/2022
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
We apply preference modeling and reinforcement learning from human feedb...
research
∙
02/15/2022
Predictability and Surprise in Large Generative Models
Large-scale pre-training has recently emerged as a technique for creatin...
research
∙
10/04/2021
An Empirical Investigation of Learning from Biased Toxicity Labels
Collecting annotations from human raters often results in a trade-off be...
research
∙
02/17/2021
