The advent of large language models marks a revolutionary breakthrough in artificial
intelligence. With the unprecedented scale of training and model parameters, the capability …

Causal inference has shown potential in enhancing the predictive accuracy, fairness,
robustness, and explainability of Natural Language Processing (NLP) models by capturing …

Through considerable effort and intuition, several recent works have reverse-engineered
nontrivial behaviors oftransformer models. This paper systematizes the mechanistic …

Abstract Concept erasure aims to remove specified features from a representation. It can
improve fairness (eg preventing a classifier from using gender or race) and interpretability …

This paper introduces Radiology-Llama2, a large language model specialized for radiology
through a process known as instruction tuning. Radiology-Llama2 is based on the Llama2 …

\emph {Circuit analysis} is a promising technique for understanding the internal mechanisms
of language models. However, existing analyses are done in small models far from the state …

Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …

Interpretable machine learning has exploded as an area of interest over the last decade,
sparked by the rise of increasingly large datasets and deep neural networks …

Mechanistic interpretability seeks to understand the internal mechanisms of machine
learning models, where localization--identifying the important model components--is a key …

There is much excitement about the opportunity to harness the power of large language
models (LLMs) when building problem-solving assistants. However, the standard …