| CPC G06N 3/0475 (2023.01) [G06N 3/08 (2013.01)] | 17 Claims |
|
1. A computing system for soft prompt tuning for proactive content generation, the system comprising:
one or more processors; and
one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising:
obtaining input data, wherein the input data is descriptive of a particular user accessing a user interface;
obtaining, in response to obtaining the input data, a soft prompt associated with the particular user from a profile database, wherein the profile database stores a plurality of user-specific sets of parameters associated with a plurality of different users, wherein each user-specific set of parameters of the plurality of user-specific sets of parameters was tuned for user-specific content generation for a different respective user, wherein the soft prompt comprises a set of parameters, wherein the set of parameters comprise machine-learned weights;
obtaining search history data associated with the particular user, wherein the search history data is descriptive of a plurality of previous search queries associated with the particular user;
determining the search history data is associated with a particular topic;
generating a prompt input based on the particular topic;
processing the soft prompt and the prompt input with a machine-learned content generation model to generate a model-generated content item, wherein the model-generated content item is generated based on the set of parameters associated with the particular user, wherein the model-generated content item comprises model-generated fiction comprising one or more style attributes determined based on the soft prompt associated with the particular user, and wherein the machine-learned content generation model comprises a pre-trained generative model, wherein a model-generated content item style for the model-generated content item is determined based on the set of parameters of the soft prompt associated with the particular user, and wherein a topic of the model-generated content item is conditioned based on the prompt input;
providing the model-generated content item to the particular user via the user interface;
generating feedback data based on data retrieved from a user computing system via the user interface, wherein the feedback data is associated with one or more interactions with the model-generated content item; and
adjusting a subset of the set of parameters of the soft prompt associated with the particular user based on the feedback data, wherein adjusting the subset of the set of parameters tunes at least a subset of the machine-learned weights of the soft prompt to adjust style attribute conditioning.
|