24 citations · 95 across the 28 of their papers we have counts for
6 papers · 1 filter
Linear Representations of Sentiment in Large Language Models
Curt Tigges, Oskar John Hollinsworth, Atticus Geiger +1
Sentiment is a pervasive feature in natural language text, yet it is an open question how sentiment is represented within Large Language Models (LLMs). In this study, we reveal tha…
Rigorously Assessing Natural Language Explanations of Neurons
Jing Huang, Atticus Geiger, Karel D'Oosterlinck +2
Natural language is an appealing medium for explaining how large language models process and store information, but evaluating the faithfulness of such explanations is challenging.…
ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning
Jingyuan Selena She, Christopher Potts, Samuel R. Bowman +1
A number of recent benchmarks seek to assess how well models handle natural language negation. However, these benchmarks lack the controlled example paradigms that would allow us t…
Interpretability at Scale: Identifying Causal Mechanisms in Alpaca
Zhengxuan Wu, Atticus Geiger, Thomas Icard +2
Obtaining human-interpretable explanations of large, general-purpose language models is an urgent goal for AI safety. However, it is just as important that our interpretability met…
Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations
Atticus Geiger, Zhengxuan Wu, Christopher Potts +2
Causal abstraction is a promising theoretical framework for explainable artificial intelligence that defines when an interpretable high-level causal model is a faithful simplificat…
Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability
Atticus Geiger, Duligur Ibeling, Amir Zur +8
Causal abstraction provides a theoretical foundation for mechanistic interpretability, the field concerned with providing intelligible algorithms that are faithful simplifications…