REALM is a retrieval-augmented Transformer architecture that bridges the gap between traditional language models and information retrieval systems. It combines three coordinated components: Retriever – searches a large external corpus (e.g., Wikipedia) for evidence passages. Knowledge-Augmented Encoder – reads both ...

LaMDA (Language Model for Dialogue Applications) is a Transformer-based model developed by Google, trained on over 1.56 trillion words of dialogue and web text. At its peak, it scaled to 137 billion parameters, making it one of the most extensive ...

CALM is a decoding strategy that adapts computation based on token difficulty. Instead of forcing every token to pass through the full stack of layers, CALM introduces confidence-based checkpoints. If the model is confident early, it stops processing deeper layers. ...

At its core, conversational search transforms information retrieval into a multi-turn dialogue rather than a one-off query-response. Instead of reformulating the same keywords, users can: Ask naturally: “Who is the CEO of Tesla?” Follow up: “How old is he?” Clarify: ...

A contextual border is a boundary of meaning — the invisible line that separates one knowledge domain from another. In NLP, this often shows up in topic segmentation tasks. In SEO, it parallels topical borders, which define the scope of ...

Golden Embeddings are multi-dimensional vector representations that combine semantic similarity, entity relationships, user intent, trust signals, and freshness thresholds. Unlike traditional embeddings, they aim to reduce semantic friction by aligning queries, content, and entities through credibility and context — delivering ...

PEGASUS is a Transformer-based sequence-to-sequence model designed specifically for abstractive summarization. Instead of training on generic text-prediction tasks, PEGASUS learns through a unique approach called Gap-Sentence Generation (GSG) — predicting the most important sentences that were deliberately removed from a ...

A Contextual Bridge is a deliberate connection between two different but related topics, entities, or content clusters. It serves three main functions: Linking related content → guiding users to adjacent but distinct pages in a semantic content network. Maintaining flow ...

A Contextual Flow is the deliberate structuring of ideas, entities, and topics so they connect naturally without abrupt breaks. It ensures that each idea builds upon the previous one, forming a chain of meaning that feels natural to both readers ...

Contextual Coverage refers to the breadth and depth of topical inclusion within a piece (or cluster) of content. It is not about stuffing keywords — it is about mapping the semantic space and ensuring no relevant question is left unanswered. ...