Attributing generated textual contents to training examples

A system for attributing generated textual contents to training examples, the system comprising:

• at least one processor configured to perform operations, the operations comprising:
• receiving a first textual content generated using a generative model, the generative model is a result of training a machine learning model using a plurality of training examples, each training example of the plurality of training examples is associated with a respective textual content;
• determining one or more properties of the first textual content;
• for each training example of the plurality of training examples, determining one or more properties of the respective textual content;
• and
• using the one or more properties of the first textual content and the properties of the textual contents associated with the plurality of training examples to attribute the first textual content to a first subgroup of at least one but not all of the plurality of training examples.

A method for attributing generated textual contents to training examples, the method comprising:

• receiving a first textual content generated using a generative model, the generative model is a result of training a machine learning model using a plurality of training examples, each training example of the plurality of training examples is associated with a respective textual content;
• determining one or more properties of the first textual content;
• for each training example of the plurality of training examples, determining one or more properties of the respective textual content;
• and
• using the one or more properties of the first textual content and the properties of the textual contents associated with the plurality of training examples to attribute the first textual content to a first subgroup of at least one but not all of the plurality of training examples.