Results for "meaning-based retrieval"

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

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

Breaking documents into pieces for retrieval; chunk size/overlap strongly affect RAG quality.

Coordinating tools, models, and steps (retrieval, calls, validation) to deliver reliable end-to-end behavior.

A function measuring prediction error (and sometimes calibration), guiding gradient-based optimization.

Architecture based on self-attention and feedforward layers; foundation of modern LLMs and many multimodal models.

A preference-based training method optimizing policies directly from pairwise comparisons without explicit RL loops.

A measure of a model class’s expressive capacity based on its ability to shatter datasets.

Probabilistic energy-based neural network with hidden variables.

Continuous loop adjusting actions based on state feedback.

Sampling-based motion planner.

Prompt augmented with retrieved documents.

Models that define an energy landscape rather than explicit probabilities.

Learns the score (∇ log p(x)) for generative sampling.

Exact likelihood generative models using invertible transforms.

RL using learned or known environment models.