Understanding retrieval and vectors
Retrieval-augmented generation (RAG) is a technique that enhances text generation by retrieving and incorporating external knowledge. This grounds the output in factual information rather than relying solely on the knowledge that is encoded in the language model’s parameters. Retrieval-Augmented Language Models (RALMs) specifically refer to retrieval-augmented language models that integrate retrieval into the training and inference process.
Traditional language models generate text autoregressively based only on the prompt. RALMs augment this by first retrieving relevant context from external corpora using semantic search algorithms. Semantic search typically involves indexing documents into vector embeddings, allowing fast similarity lookups via approximate nearest neighbor search.
The retrieved evidence then conditions the language model to produce more accurate, contextually relevant text. This cycle repeats, with RALMs formulating...
