Unsur-prisingly, however, models struggle to compose syntactically valid programs in programming languages unrepresented in pre-training, referred to as very low-resource Programming Languages (VLPLs).

Monocular depth estimation (MDE) is fundamental for deriving 3D scene structures from 2D images.

Safety and trustworthiness are indispensable requirements for real-world applications of AI systems using large language models (LLMs).

Ouyang et al., 2022, Bai et al., 2022], achieves this by learning a reward model on human preference

Why is this the case? One possibility is that some aspect of the transformer architecture makes these behaviors easy to learn. Under this hypothesis, transformer models do not implement any useful functionality when initialized; however, their loss landscape is structured such that they can be (computation-and sample-) efficiently optimized for behaviors of interest.

Can incorporating context into self-supervised vision algorithms eliminate augmentation-based inductive priors and enable dynamic adaptation to varying task symmetries? This work suggests a positive answer to this question by proposing to enhance the current joint embedding architecture with a finite context -- an abstract representation of a task, containing a few demonstrations that inform about task-specific symmetries, as shown in Figure 2(c).

Given a piece of subject text, many existing detection methods work by measuring the difficulty of LLM predicting the next token in the text from their prefix.

Most widely adopted MoE models are discontinuous with respect to their parameters - often referred to as sparse .

Beginning with the simplification of flattening all actions, we theoretically explore the discrepancies between action-level optimization and this naive token-level optimization.