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Hi, I'm Aaron Mueller! 👋

I'm a Zuckerman post-doctoral fellow working with Yonatan Belinkov and David Bau. I'm interested in NLP, robust generalization, interpreting deep neural models of language, and targeted model editing. My recent work has focused on causal probing methods, evaluating the inductive biases of pre-trained models, and understanding what, exactly, pre-training teaches models about the underlying structure of language.

For more information, please check out:

Aaron Mueller's Projects

clams icon clams

Syntactic evaluation sets, attribute-varying grammars, and code for replicating the CLAMS paper. ACL 2020.

dont-stop-pretraining icon dont-stop-pretraining

Adapting the Don't Stop Pretraining approach for multilingual applications. Modified by Aaron Mueller and Nathaniel Weir.

dotfiles icon dotfiles

Config files for easy setup on new UNIX-based machines

mbert-docclass icon mbert-docclass

Investigation of different methods of multilingual fine-tuning for document classification with mBERT.

minicons icon minicons

Utility for analyzing Transformer based representations of language.

mt-decoders icon mt-decoders

Basic IBM-style machine translation models with various decoding methods.

nshell icon nshell

nshell: a basic shell environment written in C

parlai-hred icon parlai-hred

Implementation of Hierarchical Recurrent Encoder-Decoder (HRED) model for narrative generation in ParlAI.

sparse_coding icon sparse_coding

Using sparse coding to find distributed representations used by neural networks.

syntax-icl icon syntax-icl

Code and data for In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax

transductions icon transductions

A PyTorch framework for creating, running, and reproducing experiments on seq2seq models.

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