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ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, [1] is a set of metrics and a software package used for evaluating automatic summarization and machine translation software in natural language processing.
Data redundancy in massive collections, such as the web, means that nuggets of information are likely to be phrased in many different ways in differing contexts and documents, [10] leading to two benefits: If the right information appears in many forms, the question answering system needs to perform fewer complex NLP techniques to understand ...
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.
Natural-language programming (NLP) is an ontology-assisted way of programming in terms of natural-language sentences, e.g. English. [1] A structured document with Content, sections and subsections for explanations of sentences forms a NLP document, which is actually a computer program. Natural language programming is not to be mixed up with ...
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Quality is considered to be the correspondence between a machine's output and that of a human: "the closer a machine translation is to a professional human translation, the better it is" – this is the central idea behind BLEU.
Though RLHF does not require massive amounts of data to improve performance, sourcing high-quality preference data is still an expensive process. Furthermore, if the data is not carefully collected from a representative sample, the resulting model may exhibit unwanted biases. High-level overview of reinforcement learning from human feedback
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.