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  2. Résumé - Wikipedia

    en.wikipedia.org/wiki/Résumé

    An example of a résumé with a common format with the name John Doe. A résumé or resume (or alternatively resumé), [a] [1] is a document created and used by a person to present their background, skills, and accomplishments. Résumés can be used for a variety of reasons, but most often are used to secure new jobs, whether in the same ...

  3. Automatic summarization - Wikipedia

    en.wikipedia.org/wiki/Automatic_summarization

    For example, the set cover problem is a special case of submodular optimization, since the set cover function is submodular. The set cover function attempts to find a subset of objects which cover a given set of concepts. For example, in document summarization, one would like the summary to cover all important and relevant concepts in the document.

  4. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words.

  5. Applications of randomness - Wikipedia

    en.wikipedia.org/wiki/Applications_of_randomness

    For these applications, truly random numbers are ideal, and very high quality pseudo-random numbers are necessary if truly random numbers, such as coming from a hardware random number generator, are unavailable. Truly random numbers are absolutely required to be assured of the theoretical security provided by the one-time pad — the only ...

  6. Cover letter - Wikipedia

    en.wikipedia.org/wiki/Cover_letter

    Job seekers frequently send a cover letter along with their curriculum vitae or applications for employment as a way of introducing themselves to potential employers and explaining their suitability for the desired positions. [2] It is a pitch describing one's interest in the position, skills and relevant experience for the advertised job.

  7. Retrieval-augmented generation - Wikipedia

    en.wikipedia.org/wiki/Retrieval-augmented_generation

    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.