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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 ...
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
Stemming; Full-text indexing instead of only indexing keywords or abstracts; Indexing text on inbound links from other documents (or other social tagging); Query expansion.A 2012 study by Zhao and Callan [3] using expert created manual conjunctive normal form queries has shown that searchonym expansion in the Boolean conjunctive normal form is much more effective than the traditional bag of ...
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.
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.
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.
Subword tokenisation introduces a number of quirks in LLMs, such as failure modes where LLMs can't spell words, reverse certain words, handle rare tokens, which are not present in byte-level tokenisation.