Modeling Color Terminology Across Thousands of Languages. - Arya D. McCarthy; Winston Wu; Aaron Mueller; Bill Watson; David Yarowsky (EMNLP/IJCNLP (1), 2019)

Quantity doesn't buy quality syntax with neural language models. - Marten van Schijndel; Aaron Mueller; Tal Linzen (EMNLP/IJCNLP (1), 2019)

Cross-Linguistic Syntactic Evaluation of Word Prediction Models. - Aaron Mueller; Garrett Nicolai; Panayiota Petrou-Zeniou; Natalia Talmina; Tal Linzen (ACL, 2020)

The Johns Hopkins University Bible Corpus: 1600+ Tongues for Typological Exploration. - Arya D. McCarthy; Rachel Wicks; Dylan Lewis; Aaron Mueller; Winston Wu; Oliver Adams; Garrett Nicolai; Matt Post; David Yarowsky (LREC, 2020)

An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages. - Aaron Mueller; Garrett Nicolai; Arya D. McCarthy; Dylan Lewis; Winston Wu; David Yarowsky (LREC, 2020)

Fine-grained Morphosyntactic Analysis and Generation Tools for More Than One Thousand Languages. - Garrett Nicolai; Dylan Lewis; Arya D. McCarthy; Aaron Mueller; Winston Wu; David Yarowsky (LREC, 2020)

Decoding Methods for Neural Narrative Generation. - Alexandra DeLucia; Aaron Mueller; Xiang Lisa Li; João Sedoc (CoRR, 2020)

Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models. - Matthew Finlayson; Aaron Mueller; Sebastian Gehrmann; Stuart M. Shieber; Tal Linzen; Yonatan Belinkov (ACL/IJCNLP (1), 2021)

Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling. - Aaron Mueller; Mark Dredze (NAACL-HLT, 2021)

Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement. - Aaron Mueller; Zach Wood-Doughty; Silvio Amir; Mark Dredze; Alicia Lynn Nobles (Proc. ACM Hum. Comput. Interact., 2021)

Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models. - Aaron Mueller; Robert Frank 001; Tal Linzen; Luheng Wang; Sebastian Schuster (ACL (Findings), 2022)

Label Semantic Aware Pre-training for Few-shot Text Classification. - Aaron Mueller; Jason Krone; Salvatore Romeo; Saab Mansour; Elman Mansimov; Yi Zhang 001; Dan Roth (ACL (1), 2022)

Causal Analysis of Syntactic Agreement Neurons in Multilingual Language Models. - Aaron Mueller; Yu Xia; Tal Linzen (CoNLL, 2022)

Bernice: A Multilingual Pre-trained Encoder for Twitter. - Alexandra DeLucia; Shijie Wu; Aaron Mueller; Carlos Alejandro Aguirre; Philip Resnik; Mark Dredze (EMNLP, 2022)

What Do NLP Researchers Believe? Results of the NLP Community Metasurvey. - Julian Michael; Ari Holtzman; Alicia Parrish; Aaron Mueller; Alex Wang; Angelica Chen; Divyam Madaan; Nikita Nangia; Richard Yuanzhe Pang; Jason Phang; Samuel R. Bowman (ACL (1), 2023)

How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases. - Aaron Mueller; Tal Linzen (ACL (1), 2023)

Meta-training with Demonstration Retrieval for Efficient Few-shot Learning. - Aaron Mueller; Kanika Narang; Lambert Mathias; Qifan Wang 001; Hamed Firooz (ACL (Findings), 2023)

Language model acceptability judgements are not always robust to context. - Koustuv Sinha; Jon Gauthier; Aaron Mueller; Kanishka Misra; Keren Fuentes; Roger Levy; Adina Williams (ACL (1), 2023)

Call for Papers - The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus. - Alex Warstadt; Leshem Choshen; Aaron Mueller; Adina Williams; Ethan Wilcox; Chengxu Zhuang (CoRR, 2023)

Inverse Scaling: When Bigger Isn't Better. - Ian R. McKenzie; Alexander Lyzhov; Michael Pieler; Alicia Parrish; Aaron Mueller; Ameya Prabhu; Euan McLean; Aaron Kirtland; Alexis Ross; Alisa Liu; Andrew Gritsevskiy; Daniel Wurgaft; et al. (Trans. Mach. Learn. Res., 2023)

Function Vectors in Large Language Models. - Eric Todd; Millicent L. Li; Arnab Sen Sharma; Aaron Mueller; Byron C. Wallace; David Bau (ICLR, 2024)

In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax. - Aaron Mueller; Albert Webson; Jackson Petty; Tal Linzen (NAACL-HLT, 2024)

[Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus. - Leshem Choshen; Ryan Cotterell; Michael Y. Hu; Tal Linzen; Aaron Mueller; Candace Ross; Alex Warstadt; Ethan Wilcox; Adina Williams; Chengxu Zhuang (CoRR, 2024)

Missed Causes and Ambiguous Effects: Counterfactuals Pose Challenges for Interpreting Neural Networks. - Aaron Mueller (CoRR, 2024)

NNsight and NDIF: Democratizing Access to Foundation Model Internals. - Jaden Fiotto-Kaufman; Alexander R. Loftus; Eric Todd; Jannik Brinkmann; Caden Juang; Koyena Pal; Can Rager; Aaron Mueller; Samuel Marks; Arnab Sen Sharma; Francesca Lucchetti; Michael Ripa; et al. (CoRR, 2024)

The Quest for the Right Mediator: A History, Survey, and Theoretical Grounding of Causal Interpretability. - Aaron Mueller; Jannik Brinkmann; Millicent L. Li; Samuel Marks; Koyena Pal; Nikhil Prakash; Can Rager; Aruna Sankaranarayanan; Arnab Sen Sharma; Jiuding Sun; Eric Todd; David Bau; et al. (CoRR, 2024)

Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora. - Michael Y. Hu; Aaron Mueller; Candace Ross; Adina Williams; Tal Linzen; Chengxu Zhuang; Ryan Cotterell; Leshem Choshen; Alex Warstadt; Ethan Gotlieb Wilcox (CoRR, 2024)

Position-aware Automatic Circuit Discovery. - Tal Haklay; Hadas Orgad; David Bau; Aaron Mueller; Yonatan Belinkov (ACL (1), 2025)

NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals. - Jaden Fried Fiotto-Kaufman; Alexander Russell Loftus; Eric Todd; Jannik Brinkmann; Koyena Pal; Dmitrii Troitskii; Michael Ripa; Adam Belfki; Can Rager; Caden Juang; Aaron Mueller; Samuel Marks; et al. (ICLR, 2025)

Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models. - Samuel Marks; Can Rager; Eric J. Michaud; Yonatan Belinkov; David Bau; Aaron Mueller (ICLR, 2025)

Arithmetic Without Algorithms: Language Models Solve Math with a Bag of Heuristics. - Yaniv Nikankin; Anja Reusch; Aaron Mueller; Yonatan Belinkov (ICLR, 2025)

Large Language Models Share Representations of Latent Grammatical Concepts Across Typologically Diverse Languages. - Jannik Brinkmann; Chris Wendler; Christian Bartelt; Aaron Mueller (NAACL (Long Papers), 2025)

Incremental Sentence Processing Mechanisms in Autoregressive Transformer Language Models. - Michael Hanna 001; Aaron Mueller (NAACL (Long Papers), 2025)

Characterizing the Role of Similarity in the Property Inferences of Language Models. - Juan Diego Rodriguez; Aaron Mueller; Kanishka Misra (NAACL (Long Papers), 2025)

Disjoint Processing Mechanisms of Hierarchical and Linear Grammars in Large Language Models. - Aruna Sankaranarayanan; Dylan Hadfield-Menell; Aaron Mueller (CoRR, 2025)

BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop. - Lucas Charpentier; Leshem Choshen; Ryan Cotterell; Mustafa Omer Gul; Michael Y. Hu; Jaap Jumelet; Tal Linzen; Jing Liu; Aaron Mueller; Candace Ross; Raj Sanjay Shah; Alex Warstadt; et al. (CoRR, 2025)

Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora. - Alex Warstadt; Aaron Mueller; Leshem Choshen; Ethan Wilcox; Chengxu Zhuang; Juan Ciro; Rafael Mosquera; Bhargavi Paranjape; Adina Williams; Tal Linzen; Ryan Cotterell (CoRR, 2025)

MIB: A Mechanistic Interpretability Benchmark. - Aaron Mueller; Atticus Geiger; Sarah Wiegreffe; Dana Arad; Iván Arcuschin; Adam Belfki; Yik Siu Chan; Jaden Fiotto-Kaufman; Tal Haklay; Michael Hanna 001; Jing Huang; Rohan Gupta; et al. (CoRR, 2025)

SAEs Are Good for Steering - If You Select the Right Features. - Dana Arad; Aaron Mueller; Yonatan Belinkov (CoRR, 2025)

How to Improve the Robustness of Closed-Source Models on NLI. - Joe Stacey; Lisa Alazraki; Aran Ubhi; Beyza Ermis; Aaron Mueller; Marek Rei (CoRR, 2025)