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It consists of about 16,000 multiple-choice questions spanning 57 academic subjects including mathematics, philosophy, law, and medicine. It is one of the most commonly used benchmarks for comparing the capabilities of large language models, with over 100 million downloads as of July 2024.
One strategy to attempt to box the AI would be to allow it to respond to narrow multiple-choice questions whose answers would benefit human science or medicine, but otherwise bar all other communication with, or observation of, the AI. [20]
The high-level architecture of IBM's DeepQA used in Watson [9]. Watson was created as a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering.
Google’s transition into an AI-powered answer engine is a bulwark against an emergent AI threat. ... “There are still more questions than answers as to how Google's search ad revenues will ...
Question answering systems in the context of [vague] machine reading applications have also been constructed in the medical domain, for instance related to [vague] Alzheimer's disease. [3] Open-domain question answering deals with questions about nearly anything and can only rely on general ontologies and world knowledge. Systems designed for ...
Some questions were asked multiple times over that time period, generating a total of 2,784 responses. According to their analysis, Google’s Gemini 1.0 Pro initially responded with correct ...
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." [122]).
That is, after pre-training, BERT can be fine-tuned with fewer resources on smaller datasets to optimize its performance on specific tasks such as natural language inference and text classification, and sequence-to-sequence-based language generation tasks such as question answering and conversational response generation.
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related to: ai for answering multiple choice questions