4 papers
Learning a Generative Meta-Model of LLM Activations
Grace Luo, Jiahai Feng, Trevor Darrell +2
Existing approaches for analyzing neural network activations, such as PCA and sparse autoencoders, rely on strong structural assumptions. Generative models offer an alternative: th…
Constantly Improving Image Models Need Constantly Improving Benchmarks
Jiaxin Ge, Grace Luo, Heekyung Lee +7
Recent advances in image generation, often driven by proprietary systems like GPT-4o Image Gen, regularly introduce new capabilities that reshape how users interact with these mode…
Dual-Process Image Generation
Grace Luo, Jonathan Granskog, Aleksander Holynski +1
Prior methods for controlling image generation are limited in their ability to be taught new tasks. In contrast, vision-language models, or VLMs, can learn tasks in-context and pro…
Vision-Language Models Create Cross-Modal Task Representations
Grace Luo, Trevor Darrell, Amir Bar
Autoregressive vision-language models (VLMs) can handle many tasks within a single model, yet the representations that enable this capability remain opaque. We find that VLMs align…