Embedding

Automatically learning useful internal features (latent variables) that capture salient structure for downstream tasks.

A datastore optimized for similarity search over embeddings, enabling semantic retrieval at scale.

Retrieval based on embedding similarity rather than keyword overlap, capturing paraphrases and related concepts.

When some classes are rare, requiring reweighting, resampling, or specialized metrics.

Training one model on multiple tasks simultaneously to improve generalization through shared structure.

Methods that learn training procedures or initializations so models can adapt quickly to new tasks with little data.

A structured collection of examples used to train/evaluate models; quality, bias, and coverage often dominate outcomes.

Automatically learning useful internal features (latent variables) that capture salient structure for downstream tasks.