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Machine learning based query term weight and synonym analyzer for query expansion. LucQE - open-source, Java. Provides a framework along with several implementations that allow to perform query expansion with the use of Apache Lucene. Xapian is an open-source search library which includes support for query expansion; ReQue open-source, Python ...
Retrieval Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.As language models, LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a self-supervised and semi-supervised training process.
The time complexity for training and testing the algorithm are listed below and followed by the definition of each variable.Note that when in testing phase, the time complexity can be reduced to that of calculating the euclidean distance between a class centroid and the respective document.
This page in a nutshell: Avoid using large language models (LLMs) to write original content or generate references. LLMs can be used for certain tasks (like copyediting, summarization, and paraphrasing) if the editor has substantial prior experience in the intended task and rigorously scrutinizes the results before publishing them.
We can generalize the previous 2D extended Boolean model example to higher t-dimensional space using Euclidean distances. This can be done using P-norms which extends the notion of distance to include p-distances, where 1 ≤ p ≤ ∞ is a new parameter.
Over the years, query logs have become a rich resource which contains Web users' knowledge about the World Wide Web. Query clustering method [4] tries to associate related queries by clustering "session data", which contain multiple queries and click-through information from a single user interaction. They take into account terms from result ...
The query likelihood model is a language model [1] [2] used in information retrieval. A language model is constructed for each document in the collection. It is then possible to rank each document by the probability of specific documents given a query. This is interpreted as being the likelihood of a document being relevant given a query.