Abstract: Generally discussed herein are devices, systems, and methods for generating an automatic interactive digital notebook completion model. A method can include receiving notebook content of an interactive digital notebook, the notebook content including a markdown cell followed by a code cell. The method can include generating input/output examples by, for each input/output example by masking one of (i) content of the markdown cell or (ii) content of the code cell resulting in a masked cell, identifying the masked cell and content of another cell of the markdown cell or the code that is not masked as an input for an input/output example, and identifying the content of the masked cell as an output for the input/output example. The method can include training, based on the input/output examples, a natural language processing model that generates a prediction of the content of a second masked cell as an output.

Abstract: Embodiments relate to an artificial intelligence (AI) computer platform to incorporate synthetic data and ground truth data, and to promote diversity and accuracy in generating the synthetic data. Synthetic questions are generated by a question generator in response to semantically related ground truth passage and answer data. Each generated question is presented to an answer generator together with the semantically related ground truth passage. Each synthetic question is evaluated with respect to its diversity from previous synthetic questions generated for the same ground truth passage and answer data. Each synthetic question is also evaluated with respect to the accuracy of the answer generated by the answer generator. A reward function that captures both accuracy and diversity of each synthetic question is leveraged to selectively modify the question generator, with the selective modification(s) directed at increasing textual diversity and maintaining accuracy of the generated synthetic questions.

Abstract: Generally discussed herein are devices, systems, and methods for generating an automatic interactive digital notebook completion model. A method can include receiving notebook content of an interactive digital notebook, the notebook content including a markdown cell followed by a code cell. The method can include generating input/output examples by, for each input/output example by masking one of (i) content of the markdown cell or (ii) content of the code cell resulting in a masked cell, identifying the masked cell and content of another cell of the markdown cell or the code that is not masked as an input for an input/output example, and identifying the content of the masked cell as an output for the input/output example. The method can include training, based on the input/output examples, a natural language processing model that generates a prediction of the content of a second masked cell as an output.

Abstract: Embodiments relate to an artificial intelligence (AI) computer platform to incorporate synthetic data and ground truth data, and to promote diversity and accuracy in generating the synthetic data. Synthetic questions are generated by a question generator in response to semantically related ground truth passage and answer data. Each generated question is presented to an answer generator together with the semantically related ground truth passage. Each synthetic question is evaluated with respect to its diversity from previous synthetic questions generated for the same ground truth passage and answer data. Each synthetic question is also evaluated with respect to the accuracy of the answer generated by the answer generator. A reward function that captures both accuracy and diversity of each synthetic question is leveraged to selectively modify the question generator, with the selective modification(s) directed at increasing textual diversity and maintaining accuracy of the generated synthetic questions.