Abstract: The techniques described herein enhance the operations of natural language generation systems through training and/or augmentation by a large language model. In a first example, the large language model can execute training operations by processing a training dataset to produce a natural language output. The natural language generation system can analyze the training dataset and the natural language output to generate a natural language output mimicking the output of the large language model. The large language model can then evaluate the output of the natural language generation system to iteratively adjust and improve the quality of natural language outputs. In a second example, the large language can augment a small language model in executing natural language tasks. This is accomplished by retrieving external information using the large language model to generate an augmentation input to provide context and a language framework to the small language model to enhance overall outputs.

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: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that facilitate identifying entity-attribute relationships in text corpora. Methods include determining whether an attribute in a candidate entity-attribute pair is an actual attribute of the entity in the entity-attribute candidate pair. This includes generating embeddings for words in the set of sentences that include the entity and the attribute and generating, using known entity-attribute pairs. This also includes generating an attribute distributional embedding for the entity based on other attributes associated with the entity from the known entity-attribute pairs, and generating an attribute distributional embedding for the attribute based on known attributes associated with known entities of the attribute in the known entity-attribute pairs.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that facilitate identifying entity-attribute relationships in text corpora. Methods include determining whether an attribute in a candidate entity-attribute pair is an actual attribute of the entity in the entity-attribute candidate pair. This includes generating embeddings for words in the set of sentences that include the entity and the attribute and generating, using known entity-attribute pairs. This also includes generating an attribute distributional embedding for the entity based on other attributes associated with the entity from the known entity-attribute pairs, and generating an attribute distributional embedding for the attribute based on known attributes associated with known entities of the attribute in the known entity-attribute pairs.