Abstract: The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

Abstract: The systems and methods disclosed herein enable mapping of gaps in controls to operative standards. The system receives an output generation request using an artificial intelligence (AI) model, where the input includes a set of gaps associated with one or more scenarios failing to satisfy the operative standards of a set of vector representations. Each gap in the set of gaps includes attributes defining the scenario. Using the received input, the system constructs prompts for each gap, where the prompts include information related to the scenario and/or the operative standards. Each prompt compares the corresponding gap against the operative standards or the set of vector representations. For each gap, the system maps the gap to the operative standards by supplying the prompt of the particular gap into the AI model and, in response, receiving from the AI model the operative standards associated with the particular gap.

Abstract: The systems and methods disclosed herein receive an output generation request from that includes input for generating an output using a language model. The input includes a set of alphanumeric characters associated with operative standards for a first set of actions. The system divides the set of alphanumeric characters into text subsets. For each text subset, a vector representation is determined. Prompts are created for each vector representation including the set of alphanumeric characters, query contexts, keywords, and/or the text subset. Each vector representation's prompt is input into the language model, which generates a second set of actions of related actions, where subsequently generated actions are based on prior generated actions. The system aggregates the second set of actions into a third set of actions and displays a graphical layout. The graphical layout displays a representation of the set of alphanumeric characters and the corresponding actions.

Abstract: The systems and methods disclosed herein relate to a model validation platform that enables dynamic validation of a user's prompt for a large language model (LLM) in order to evaluate the validity of the prompt and the suitability of a large language model for processing the prompt. For example, the platform enables an estimation of the resource allocation associated with processing the prompt with a given LLM, as well as a modification of the prompt, prior to the processing the prompt with the selected LLM. The platform can further validate the output prior to transmitting the output to a server system for display to the user. By doing so, the platform enables dynamic evaluation of a request to execute an LLM, as well as evaluation of resulting outputs, for accuracy and efficiency improvements in data processing or software development pipelines.

Abstract: The systems and methods disclosed herein enable evaluation of machine learning model outputs within a virtual environment. The disclosed model validation platform enables testing of code generated for detection of malicious or anomalous outputs. For example, the model validation platform can construct a virtual machine isolated from the system and test model-generated code for validation of LLM-generated outputs. In some implementations, the model validation platform determines parameters of the virtual machine and/or associated validation test based on an evaluation of the machine learning model's output and/or the associated underlying prompt. For example, the parameters of the validation test depend on an evaluation of the user or the provided input (e.g., depending on the presence of sensitive data within the prompt). By doing so, the system enables dynamic evaluation of machine learning model outputs to improve the security and robustness of associated generated code.

Abstract: The disclosed data generation platform enables selection of particular machine learning models on the basis of a predicted resource allocation requirement associated with a given prompt. For example, the model validation platform can evaluate the resource use (e.g., cost) associated with processing a user's prompt with a given type of model. Based on this estimated resource use, the model validation platform can route the prompt to a suitable model to optimize a performance metric value, thereby improving the efficiency of the system. In some implementations, the data generation platform trains a model to accurately estimate resource usage based on ground-truth model-related costs, thereby improving the effectiveness of model selection for efficiency improvements.