Models (LLMs) and build personalized LLMs tailored for various applications. While fine-tuning seems to be a direct solution, it requires substantial computational resources and may significantly affect the utility of the original LLM.

Object, attribute or stylistic changes can be learned from visually static datasets. On the other hand, high-quality data for action and reasoning-centric edits is scarce and has to come from entirely different sources that cover e.g.

For each prediction task, we select features that have a causal influence on the target of prediction.

In this work, we introduce ChatQA, a suite of models that outperform GPT -4 on retrieval-augmented generation (RAG) and conversational question answering (QA).

Self-attention is the key mechanism of transformers, which are the essential building blocks of modern foundation models.

Diffusion regulates numerous natural processes and the dynamics of many successful generative models.