Abstract: Solutions for evaluating source code generators use offline and online evaluation stages. Offline evaluation includes separating each of a plurality of input passages of software code into a plurality of constituent blocks. Each code generator (of a plurality of code generators) generates an equivalent block corresponding to each constituent block. A coding score is determined for each equivalent block (for each code generator), and the coding scores are aggregated across the equivalent blocks to provide an aggregate score for each code generator. A ranking of the aggregate scores is used to down-select to a fewer number of code generators for online evaluation. For this stage, the code generators output passages of software code, and user acceptance of the code generators' outputs may be used for further ranking and down-selection. Some examples weight the coding score according to a code utility estimate of the constituent blocks for which equivalent blocks are generated.

Abstract: Solutions for evaluating source code generators use offline and online evaluation stages. Offline evaluation includes separating each of a plurality of input passages of software code into a plurality of constituent blocks. Each code generator (of a plurality of code generators) generates an equivalent block corresponding to each constituent block. A coding score is determined for each equivalent block (for each code generator), and the coding scores are aggregated across the equivalent blocks to provide an aggregate score for each code generator. A ranking of the aggregate scores is used to down-select to a fewer number of code generators for online evaluation. For this stage, the code generators output passages of software code, and user acceptance of the code generators' outputs may be used for further ranking and down-selection. Some examples weight the coding score according to a code utility estimate of the constituent blocks for which equivalent blocks are generated.

Abstract: Systems and methods for directing behavior of a generative artificial intelligence (AI) system are provided. In particular, a computing device may obtain an input prompt associated with a requested task for one or more generative artificial intelligence (AI) systems, obtain one or more attributes based on the input prompt, modify the input prompt based on the one or more embedded attributes, and provide the modified input prompt to the one or more generative AI systems.

Abstract: Solutions for evaluating source code generators use offline and online evaluation stages. Offline evaluation includes separating each of a plurality of input passages of software code into a plurality of constituent blocks. Each code generator (of a plurality of code generators) generates an equivalent block corresponding to each constituent block. A coding score is determined for each equivalent block (for each code generator), and the coding scores are aggregated across the equivalent blocks to provide an aggregate score for each code generator. A ranking of the aggregate scores is used to down-select to a fewer number of code generators for online evaluation. For this stage, the code generators output passages of software code, and user acceptance of the code generators' outputs may be used for further ranking and down-selection. Some examples weight the coding score according to a code utility estimate of the constituent blocks for which equivalent blocks are generated.

Abstract: Solutions for evaluating source code generators use offline and online evaluation stages. Offline evaluation includes separating each of a plurality of input passages of software code into a plurality of constituent blocks. Each code generator (of a plurality of code generators) generates an equivalent block corresponding to each constituent block. A coding score is determined for each equivalent block (for each code generator), and the coding scores are aggregated across the equivalent blocks to provide an aggregate score for each code generator. A ranking of the aggregate scores is used to down-select to a fewer number of code generators for online evaluation. For this stage, the code generators output passages of software code, and user acceptance of the code generators' outputs may be used for further ranking and down-selection. Some examples weight the coding score according to a code utility estimate of the constituent blocks for which equivalent blocks are generated.

Abstract: Solutions for evaluating source code generators use offline and online evaluation stages. Offline evaluation includes separating each of a plurality of input passages of software code into a plurality of constituent blocks. Each code generator (of a plurality of code generators) generates an equivalent block corresponding to each constituent block. A coding score is determined for each equivalent block (for each code generator), and the coding scores are aggregated across the equivalent blocks to provide an aggregate score for each code generator. A ranking of the aggregate scores is used to down-select to a fewer number of code generators for online evaluation. For this stage, the code generators output passages of software code, and user acceptance of the code generators' outputs may be used for further ranking and down-selection. Some examples weight the coding score according to a code utility estimate of the constituent blocks for which equivalent blocks are generated.

Abstract: A system, method, and media are provided for generating a structured labeling graphical user interface. The user interface receives user input that associates multimedia content with categories. The user input may include user-supplied tags that further define the category for the multimedia content. The user-supplied tags are rendered proximate to the categories. In turn, a database logs user events to store, among other things, the categories, the user-supplied tags, time associated with completing the user-supplied tags, and time for associating multimedia content with the categories or tags.

Abstract: A system, method, and media are provided for generating a structured labeling graphical user interface. The user interface receives user input that associates multimedia content with categories. The user input may include user-supplied tags that further define the category for the multimedia content. The user-supplied tags are rendered proximate to the categories. In turn, a database logs user events to store, among other things, the categories, the user-supplied tags, time associated with completing the user-supplied tags, and time for associating multimedia content with the categories or tags.