Angelica Chen

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angelica[dot]chen[at]nyu.edu

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Selected Papers | Invited Talks | Google Scholar | Twitter

Hi! I’m a research scientist at Google DeepMind, currently working on Gemini training. My research broadly focuses on LLM training dynamics and reinforcement learning. I am also interested in the application of LLMs to biomedical fields, including both drug design and hospital decision-making. Before DeepMind, I completed my PhD at NYU in the Machine Learning for Language group, where I was fortunate to be advised by Kyunghyun Cho.

Outside of my research, I enjoy running, thrift flipping, and baking more pastries than I can feasibly eat. I also volunteer as a NYSDOH-certified rape and domestic violence crisis counselor/victim advocate for the NYC Crime Victims Treatment Center at local hospital EDs, and work with the NYC chapter of the DSA.

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Selected Papers

My work is largely split into three general directions – understanding LLM training, improving how LLMs learn from feedback, and evaluating LLMs. For a more complete list of my papers, please see Semantic Scholar.

Understanding LLM Training

Preference Learning Algorithms Do Not Learn Preference Rankings
NeurIPS 2024
Oral at ICML 2024 Workshop on Models of Human Feedback for AI Alignment (MHFAIA)
Chen, Angelica, Sadhika Malladi, Lily H. Zhang, Xinyi Chen, Qiuyi Zhang, Rajesh Ranganath, Kyunghyun Cho.
[Arxiv] [GitHub]

Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs
ICLR 2024 (Spotlight)
Chen, Angelica, Ravid Shwartz-Ziv, Kyunghyun Cho, Matthew L. Leavitt, Naomi Saphra.
[OpenReview] [Arxiv] [GitHub]

Latent State Models of Training Dynamics
Transactions on Machine Learning Research
Michael Y. Hu, Angelica Chen, Naomi Saphra, Kyunghyun Cho
[Arxiv] [OpenReview]

Improving How LLMs Learn From Feedback

Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks
ICML 2025
(Spotlight) NeurIPS 2024 Workshop on AI for New Drug Modalities (AIDrugX)
Angelica Chen, Samuel D. Stanton, Frances Ding, Robert G. Alberstein, Andrew M. Watkins, Richard Bonneau, Vladimir Gligorijević, Kyunghyun Cho, Nathan C. Frey
[Arxiv] [Github]

EvoPrompting: Language Models for Code-Level Neural Architecture Search
NeurIPS 2023 (poster)
Chen, Angelica, David M. Dohan and David R. So
[OpenReview] [Arxiv]

Learning from Natural Language Feedback
Transactions on Machine Learning Research
Chen, Angelica*, Jérémy Scheurer*, Tomasz Korbak, Jon Ander Campos, Jun Shern Chan, Samuel R. Bowman, Kyunghyun Cho, Ethan Perez
[OpenReview] [GitHub]

Pretraining Language Models with Human Preferences
ICML 2023 (oral)
Korbak, Tomasz, Kejian Shi, Angelica Chen, Rasika Bhalerao, Christopher L. Buckley, Jason Phang, Sam Bowman and Ethan Perez
[Arxiv]

Teaching BERT to Wait: Balancing Accuracy and Latency for Streaming Disfluency Detection
NAACL 2022 (oral)
Chen, Angelica, Victoria Zayats, Daniel David Walker and Dirk Ryan Padfield
[ACL Anthology]

Evaluating LLMs

Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs
Transactions on Machine Learning Research
Chen, Angelica, Jason Phang, Alicia Parrish, Vishakh Padmakumar, Chen Zhao, Samuel R. Bowman, Kyunghyun Cho.
[Arxiv] [OpenReview]

QuALITY: Question Answering with Long Input Texts, Yes!
NAACL 2022
Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, Nikita Nangia, Jason Phang, Angelica Chen, Vishakh Padmakumar, Johnny Ma, Jana Thompson, He He, Samuel Bowman
[ACL Anthology]

BBQ: A hand-built bias benchmark for question answering
ACL Findings 2022
Parrish, Alicia, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut and Sam Bowman
[ACL Anthology]

Invited Talks