27 citations · 28 across the 5 of their papers we have counts for
5 papers
The Truth Lies Somewhere in the Middle (of the Generated Tokens)
Sophie L. Wang, Phillip Isola, Brian Cheung
How should hidden states generated autoregressively be collapsed into a representation that reflects a language model's internal state? Despite tokens being generated under causal…
Words That Make Language Models Perceive
Sophie L. Wang, Phillip Isola, Brian Cheung
Large language models (LLMs) trained purely on text ostensibly lack any direct perceptual experience, yet their internal representations are implicitly shaped by multimodal regular…
Large Pretraining Datasets Don't Guarantee Robustness after Fine-Tuning in Image Classification
Jaedong Hwang, Brian Cheung, Zhang-Wei Hong +3
Large-scale pretrained models are widely leveraged as foundations for learning new specialized tasks via fine-tuning, with the goal of maintaining the general performance of the mo…
The Platonic Representation Hypothesis
Minyoung Huh, Brian Cheung, Tongzhou Wang +1
We argue that representations in AI models, particularly deep networks, are converging. First, we survey many examples of convergence in the literature: over time and across multip…
Training Neural Networks from Scratch with Parallel Low-Rank Adapters
Minyoung Huh, Brian Cheung, Jeremy Bernstein +2
The scalability of deep learning models is fundamentally limited by computing resources, memory, and communication. Although methods like low-rank adaptation (LoRA) have reduced th…