In this paper, we introduce VidProM, the first large-scale dataset comprising 1.67 M illion unique text-to-Vid eo Pro mpts from real users.

Our method comes close to the original models' performance in cross-lingual and

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.

Positional encoding plays a crucial role in transformers, significantly impacting model performance and length generalization. Prior research has introduced absolute positional encoding (APE) and relative positional encoding (RPE) to distinguish token positions in given sequences.

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.

MonoMAE consists of two novel designs.

'It's going to be really bad': Fears over AI bubble bursting grow in Silicon Valley At OpenAI's DevDay this week, OpenAI boss Sam Altman did what American tech bosses rarely do these days: he actually answered questions from reporters. I know it's tempting to write the bubble story, Mr Altman told me as he sat flanked by his top lieutenants. In fact, there are many parts of AI that I think are kind of bubbly right now. In Silicon Valley, the debate over whether AI companies are overvalued has taken on a new urgency. Sceptics are privately - and some now publicly - asking whether the rapid rise in the value of AI tech companies may be, at least in part, the result of what they call financial engineering.

Specifying reward signals that allow agents to learn complex behaviors is a longstanding challenge in reinforcement learning.