14.5k citations · 48.8k across the 185 of their papers we have counts for
205 papers
Which Preferences to Train On? End-to-End Multi-Objective Alignment with an Adversarial Preference Distribution
Minjae Lee, Kyunghyun Cho, Sangdon Park
Aligning large language models (LLMs) with human values is important for safe, efficient, and beneficial AI deployment. However, human values are multifaceted: helpfulness, harmles…
Blind Biological Sequence Denoising with Self-Supervised Set Learning
Nathan Ng, Ji Won Park, Jae Hyeon Lee +3
Biological sequence analysis relies on the ability to denoise the imprecise output of sequencing platforms. We consider a common setting where a short sequence is read out repeated…
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…
Improving Joint Speech-Text Representations Without Alignment
Cal Peyser, Zhong Meng, Ke Hu +5
The last year has seen astonishing progress in text-prompted image generation premised on the idea of a cross-modal representation space in which the text and image domains are rep…
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…
Leveraging Implicit Feedback from Deployment Data in Dialogue
Richard Yuanzhe Pang, Stephen Roller, Kyunghyun Cho +2
We study improving social conversational agents by learning from natural dialogue between users and a deployed model, without extra annotations. To implicitly measure the quality o…