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Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.
A prompt for a text-to-text language model can be a query, a command, or a longer statement including context, instructions, and conversation history. Prompt engineering may involve phrasing a query, specifying a style, choice of words and grammar, [3] providing relevant context, or describing a character for the AI to mimic. [1]
Word count is commonly used by translators to determine the price of a translation job. Word counts may also be used to calculate measures of readability and to measure typing and reading speeds (usually in words per minute). When converting character counts to words, a measure of 5 or 6 characters to a word is generally used for English. [1]
GPT-2 writing a fictional news article about Edward Snowden's actions after winning the 2020 United States presidential election (all highlighted text is machine-generated). While Snowden had (at the time of generation) never been elected to public office, the generated sample is grammatically and stylistically valid.
Prompts containing potentially objectionable content are blocked, and uploaded images are analyzed to detect offensive material. [44] A disadvantage of prompt-based filtering is that it is easy to bypass using alternative phrases that result in a similar output. For example, the word "blood" is filtered, but "ketchup" and "red liquid" are not ...
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