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The module comprises four sections, with ten questions in each section. It takes around 40 (paper-based) or 32 (computer-delivered) minutes: 30 for testing, plus 10 for transferring the answers to an answer sheet (paper-based) or 2 for re-checking the answers (computer-delivered). [21] [20] Sections 1 and 2 are about everyday, social situations.
The following is a list of websites that follow a question-and-answer format. The list contains only websites for which an article exists, dedicated either wholly or at least partly to the websites. For the humor "Q&A site" format first popularized by Forum 2000 and The Conversatron, see Q&A comedy website.
Quora is a social question-and-answer website and online knowledge market headquartered in Mountain View, California. It was founded on June 25, 2009, [5] and made available to the public on June 21, 2010. [6] Users can collaborate by editing questions and commenting on answers that have been submitted by other users. [7]
Free indirect discourse can be described as a "technique of presenting a character's voice partly mediated by the voice of the author". In the words of the French narrative theorist Gérard Genette, "the narrator takes on the speech of the character, or, if one prefers, the character speaks through the voice of the narrator, and the two instances then are merged". [1]
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.
A question answering task is considered "open book" if the model's prompt includes text from which the expected answer can be derived (for example, the previous question could be adjoined with some text which includes the sentence "The Sharks have advanced to the Stanley Cup finals once, losing to the Pittsburgh Penguins in 2016." [126]).
Sample a task prompt; Generate many reasoning traces for the prompt. Use a verifier to remove reasoning traces with the wrong final answer. For each remaining trace, extract the set of equations appearing in it. Deduplicate the traces so that each one has a different set of equations. Add those to the dataset.
Teacher #1: This answer mentions one of the required items, so it is correct. Teacher #2: This answer is correct. Teacher #1: I feel like this answer is good enough, so I'll mark it correct. Teacher #2: This answer is correct, but this good student should be able to do better than that, so I'll only give partial credit.