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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.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
Power Query is built on what was then [when?] a new query language called M.It is a mashup language (hence the letter M) designed to create queries that mix together data. It is similar to the F Sharp programming language, and according to Microsoft it is a "mostly pure, higher-order, dynamically typed, partially lazy, functional language."
Question-query pairs Question Answering 2018 [332] [333] Hartmann, Soru, and Marx et al. Vietnamese Question Answering Dataset (UIT-ViQuAD) A large collection of Vietnamese questions for evaluating MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia.
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.
The decoder sends in a query, and obtains a reply in the form of a weighted sum of the values, where the weight is proportional to how closely the query resembles each key. The decoder first processes the "<start>" input partially, to obtain an intermediate vector h 0 d {\displaystyle h_{0}^{d}} , the 0th hidden vector of decoder.
Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.