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: Technologies related to computer-implemented conditional language models (CLMs) are described. A first CLM is trained to generate output texts based upon input texts and conditions. Output texts generated by the first CLM are included in a training set, and a second CLM is trained based upon the training set. The second CLM is then configured to receive input text and a condition and generate an output text based upon the input text and the condition.

Abstract: Systems and techniques are provided for facilitating the automatic discovery and application of rules for refining the training of pretrained models, such as natural language processing models. Weak symbolic rules are automatically generated from the identification and processing of sparse labeled data by the pretrained model(s). Once the weak rules are generated, they are integrated into the model(s) via an attention mechanism to supplement the direct training performed by the sparse labeled data and to thereby boost a supervision signal generated by the sparse labeled data on any newly processed unlabeled data in the intended runtime environment(s) where the models are applied.