This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

Fine-tuning large pre-trained models has become the de facto strategy for developing both
task-specific and general-purpose machine learning systems, including developing models …

Abstract Text-to-image (T2I) diffusion models notably the unCLIP models (eg DALL-E-2)
achieve state-of-the-art (SOTA) performance on various compositional T2I benchmarks at …

Transformers trained on huge text corpora exhibit a remarkable set of capabilities, eg,
performing simple logical operations. Given the inherent compositional nature of language …

Diverse studies in systems neuroscience begin with extended periods of training known as'
shaping'procedures. These involve progressively studying component parts of more …

The literature on text-to-image generation is plagued by issues of faithfully composing
entities with relations. But there lacks a formal understanding of how entity-relation …

Transformers have shown impressive capabilities across various tasks, but their
performance on compositional problems remains a topic of debate. In this work, we …

As training datasets become increasingly drawn from unstructured, uncontrolled
environments such as the web, researchers and industry practitioners have increasingly …

Background: The findings of the 2023 AAPM Grand Challenge on Deep Generative
Modeling for Learning Medical Image Statistics are reported in this Special Report. Purpose …

We present CUPID: a visualization method for the contextual understanding of prompt‐
conditioned image distributions. CUPID targets the visual analysis of distributions produced …