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ColBERT (Contextualized Late Interaction over BERT) is a neural information retrieval model that leverages BERT embeddings combined with a novel late interaction mechanism to efficiently rank documents based on query relevance. It aims to balance retrieval effectiveness with computational efficiency.
SPLADE (sparse lexical and dense) is a neural information retrieval model designed to combine the benefits of sparse lexical representations and dense embeddings. It aims to improve search effectiveness by leveraging both term-level relevance and semantic matching.
RAPTOR is a computational framework designed for information retrieval and summarization that employs recursive abstractive processing within tree-structured data. It facilitates efficient retrieval by organizing information hierarchically and generating concise representations through recursive abstraction.
ColBERT-v2 is an advanced neural information retrieval model designed to improve efficiency and effectiveness in document ranking tasks. It builds upon the original ColBERT architecture by optimizing computational performance and enhancing retrieval accuracy.
Question answering (QA) is a field of computer science and artificial intelligence focused on building systems that automatically answer questions posed by humans in natural language. It involves retrieving, understanding, and presenting relevant information from various data sources to provide accurate and concise answers.
Precision and recall are fundamental metrics used to evaluate the performance of classification and information retrieval systems. Precision measures the accuracy of positive predictions, while recall measures the ability to identify all relevant instances.