Recall

TECHNICAL DEFINITION

A classification evaluation metric, also known as sensitivity or true positive rate, defined as the ratio of true positive predictions to the total number of actual positive instances (true positives + false negatives), indicating the model's ability to find all positive samples.

BACKGROUND

A large language model (LLM) is a neural network trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate and analyze text in many contexts, and are a foundational technology behind modern chatbots. Biased or inaccurate training data can make an LLM's output less reliable.

SYNONYMS & ALIASES

• Sensitivity
• true positive rate
• TPR

USAGE NOTE

High recall is important in applications where missing positive cases is costly, such as detecting fraudulent transactions.

DEVELOPERS

Organizations developing technology related to Recall.

• OpenAI

  Develops advanced large language models (LLMs) where 'recall' of learned information and contextual data is a critical performance metric, continuously researching and implementing improvements in model architecture and training to enhance this capability.

• Google AI (DeepMind)

  A leader in AI research and development of foundational models, focusing on enhancing their ability to accurately 'recall' and synthesize information from vast datasets and contextual inputs.

• Anthropic

  Creator of the Claude family of LLMs, dedicated to improving model performance, including the precision and breadth of information 'recall' for complex prompts and tasks, often with a focus on safety and factual accuracy.

• Cohere

  Specializes in enterprise AI, offering solutions that emphasize robust 'recall' for relevant information through advanced semantic search and Retrieval Augmented Generation (RAG) techniques to enhance LLM responses.

• LlamaIndex

  Provides a framework for building LLM applications by connecting LLMs to external data sources, specifically designed to improve an LLM's 'recall' capabilities by retrieving and providing relevant context from private or domain-specific data.

• LangChain

  An open-source framework for developing applications powered by LLMs, offering tools and components for building sophisticated Retrieval Augmented Generation (RAG) systems that directly address and improve an LLM's 'recall' of external information.

• Pinecone

  Operates a leading vector database essential for efficient and accurate information 'recall' in Retrieval Augmented Generation (RAG) systems, allowing LLMs to retrieve relevant context rapidly from large knowledge bases.

• Hugging Face

  Offers a comprehensive platform for AI development, including a vast repository of models and tools that facilitate research and implementation of techniques to improve 'recall' in LLMs and RAG systems.

• Weights & Biases

  Provides an MLOps platform for tracking, visualizing, and comparing machine learning experiments, including metrics related to 'recall' for prompt engineering, RAG evaluations, and overall LLM performance.