3 papers
cs.LG2026
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…
cs.CL2025
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…
cs.CV2025
Large Pre-Training Datasets Don't Always Guarantee Robustness after Fine-Tuning
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…