Abstract: Systems, methods and non-transitory computer readable media for chained attribution of generated content are provided. A generative model may be accessed. The generative model may be a result of training a machine learning model using a plurality of training examples. Each training example of the plurality of training examples may be associated with a respective content. An input in a natural language may be received. At least one auxiliary content may be obtained. The generative model may be used to analyze the input and the at least one auxiliary content to generate a new content, the generated new content is based on the at least one auxiliary content and at least one of the plurality of training examples. A degree of attribution of the generated new content to the at least one auxiliary content may be determined. Information based on the degree of attribution may be provided.

Abstract: Systems, methods and non-transitory computer readable media for chained attribution of generated content are provided. A generative model may be accessed. The generative model may be a result of training a machine learning model using a plurality of training examples. Each training example of the plurality of training examples may be associated with a respective content. An input in a natural language may be received. At least one auxiliary content may be obtained. The generative model may be used to analyze the input and the at least one auxiliary content to generate a new content, the generated new content is based on the at least one auxiliary content and at least one of the plurality of training examples. A degree of attribution of the generated new content to the at least one auxiliary content may be determined. Information based on the degree of attribution may be provided.

Abstract: Systems, methods and non-transitory computer readable media for attributing generated textual contents to training examples are provided. A first textual content generated using a generative model may be received. The generative model may be a result of training a machine learning model using a plurality of training examples. Each training example of the plurality of training examples may be associated with a respective textual content. Properties of the first textual content may be determined. For each training example of the plurality of training examples, properties of the respective textual content may be determined. The properties of the first textual content and the properties of the textual contents associated with the plurality of training examples may be used to attribute the first textual content to a first subgroup of at least one but not all of the plurality of training examples.

Abstract: Systems, methods and non-transitory computer readable media for generating audio clips from listening habits are provided. In some examples, a generative model may be accessed. Further, a plurality of audio clips may be accessed. For each audio clip of the plurality of audio clips, the respective audio clip may be analyzed to identify a musical property of the respective audio clip. The generative model and the identified musical properties may be used to generate a new audio clip. The new audio clip may be provided.

Abstract: Systems, methods and non-transitory computer readable media for attributing generated textual contents to training examples are provided. A first textual content generated using a generative model may be received. The generative model may be a result of training a machine learning model using a plurality of training examples. Each training example of the plurality of training examples may be associated with a respective textual content. Properties of the first textual content may be determined. For each training example of the plurality of training examples, properties of the respective textual content may be determined. The properties of the first textual content and the properties of the textual contents associated with the plurality of training examples may be used to attribute the first textual content to a first subgroup of at least one but not all of the plurality of training examples.

Abstract: Systems, methods and non-transitory computer readable media for recording usage of generated content are provided. In some examples, an indication of a usage of a specific content may be received. Further, it may be determined that the specific content is a content generated using a generative model, for example by analyzing the specific content. Further, the specific content may be analyzed to identify attribution information encoded in the specific content. The attribution information may attribute the specific content to at least one source. For each source of the at least one source, a data-record associated with the respective source may be updated based on the attribution of the specific content. Additionally, data based on the data-records may be provided.