Abstract: Systems and methods are provided for implementing quality assurance for digital technologies using language model (“LM”)-based artificial intelligence (“AI”) and/or machine learning (“ML”) systems. In various embodiments, a first prompt is provided to an LM actor or attacker to cause the LM actor or attacker to generate interaction content for interacting with test software. Responses from the test software are then evaluated by an LM evaluator to produce evaluation results. In some examples, a second prompt is generated that includes the responses from the test software along with the evaluation criteria for the test software. When the second prompt is provided to the LM evaluator, the LM evaluator generates the evaluation results.

Abstract: Generally discussed herein are devices, systems, and methods for improving architecture search and identification with constraints. A method can include receiving, at a compute device, a request for a transformer-based autoregressive language model (TBALM), the request specifying a maximum latency, identifying TBALM architectures that satisfies the maximum latency, identifying a TBALM architecture of the identified TBALM architectures that has a greatest number of decoder parameters resulting in an identified TBALM architecture, and providing the identified TBALM architecture.