565 citations · 688 across the 19 of their papers we have counts for
8 papers · 1 filter
Playing Large Games with Oracles and AI Debate
Xinyi Chen, Angelica Chen, Dean Foster +1
We consider regret minimization in repeated games with a very large number of actions. Such games are inherent in the setting of AI Safety via Debate \cite{irving2018ai}, and more…
Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs
Angelica Chen, Ravid Shwartz-Ziv, Kyunghyun Cho +2
Most interpretability research in NLP focuses on understanding the behavior and features of a fully trained model. However, certain insights into model behavior may only be accessi…
Latent State Models of Training Dynamics
Michael Y. Hu, Angelica Chen, Naomi Saphra +1
The impact of randomness on model training is poorly understood. How do differences in data order and initialization actually manifest in the model, such that some training runs ou…
Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs
Angelica Chen, Jason Phang, Alicia Parrish +4
Large language models (LLMs) have achieved widespread success on a variety of in-context few-shot tasks, but this success is typically evaluated via correctness rather than consist…
Training Language Models with Language Feedback at Scale
Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak +4
Pretrained language models often generate outputs that are not in line with human preferences, such as harmful text or factually incorrect summaries. Recent work approaches the abo…
Improving Code Generation by Training with Natural Language Feedback
Angelica Chen, Jérémy Scheurer, Tomasz Korbak +5
The potential for pre-trained large language models (LLMs) to use natural language feedback at inference time has been an exciting recent development. We build upon this observatio…
EvoPrompting: Language Models for Code-Level Neural Architecture Search
Angelica Chen, David M. Dohan, David R. So
Given the recent impressive accomplishments of language models (LMs) for code generation, we explore the use of LMs as adaptive mutation and crossover operators for an evolutionary…
Pretraining Language Models with Human Preferences
Tomasz Korbak, Kejian Shi, Angelica Chen +5
Language models (LMs) are pretrained to imitate internet text, including content that would violate human preferences if generated by an LM: falsehoods, offensive comments, persona…