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Atticus Geiger

interpretability

crowdsourcing

natural language inference

explanations

causality

intervention

adversarial datasets

mechanistic interpretability

natural language explanation

interpretability for nlp

diagnosis benchmarking

causal abstraction

sentiment analysis

causal intervention;

6

presentations

6

number of views

SHORT BIO

I'm a PhD student at Stanford using theories and tools from causality, logic, and machine learning to analyze and explain artificial neural networks.

Presentations

pyvene: A Library for Understanding and Improving PyTorch Models via Interventions

Zhengxuan Wu and 7 other authors

Rigorously Assessing Natural Language Explanations of Neurons | VIDEO

Jing Huang and 4 other authors

ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning

Jingyuan Selena She and 3 other authors

Faithful, Interpretable Model Explanations via Causal Abstraction

Atticus Geiger and 6 other authors

DynaSent: A Dynamic Benchmark for Sentiment Analysis

Christopher Potts and 3 other authors

A Semantics for Causing, Enabling, and Preventing Verbs Grounded in Structural Causal Models

Angela Cao and 4 other authors

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