However, such interleaving methods struggle to produce final results that look like natural objects of interest (i.e., manifold feasibility)

User intentions are typically formalized as evaluation rewards to be maximized when fine-tuning language models (LMs).

However, there is an ongoing debate about the extent to which LLMs are capable of actual reasoning .

The module estimates the locality score of each token from their features, before adaptively partitioning tokens into window quadrants.

With the success of deep learning, there are growing concerns over interpretability (Lipton, 2018). Ideally, the explanation should be both faithful (reflecting the model's actual behavior) and plausible

It is common to assess a model's merit for scientific discovery, and thus novel insights, by how well it aligns with already available domain

The most advanced diffusion models have recently adopted increasingly deep stacked networks (e.g., U-Net or Transformer) to promote the generative emergence capabilities of vision generation models similar to large language models (LLMs).

This task combines land cover mapping, which is closely related to semantic segmentation in computer vision, and height estimation.