Conversational interface for content creation and editing using large language models
Example embodiments of the present disclosure provide for an example method. The example method includes generating an initial user interface including a content assistant component. The example method include obtaining user input data. The example method includes processing, by a machine learned model interfacing with the content assistant component, the data indicative of the input received from the user. The method includes obtaining output data, from the machine learned model interfacing with the content assistant component, indicative of one or more content item components. The method includes transmitting data which causes the content item components to be provided for display via an updated user interface. The method includes obtaining data indicative of user selection of approval of the content item components. The method includes generating, in response to obtaining the data indicative of the user selection of the approval of the content item components, content items.
1. A computing system, comprising:
one or more processors; and
one or more non-transitory computer-readable media storing instructions that are executable to cause the one or more processors to perform operations, the operations comprising:
obtaining, via a conversational campaign assistant interface, by a custom trained machine-learned model, natural language input, wherein the natural language input comprises at least a website uniform resource locator (URL);
accessing, via the custom machine-learned model, data associated with a website associated with the website URL;
generating output comprising one or more suggested content item components based on the accessed data and natural language input;
responsive to generating the output comprising the one or more suggested content item components, automatically populating one or more input fields with the one or more suggested content item components; and
transmitting instructions that, when executed, cause a client device to provide the one or more suggested content item components for display via a user interface.
2. The computing system of claim 1 , the operations comprising:
generating an initial user interface comprising a content assistant component;
obtaining data indicative of user input;
processing, by the custom trained machine-learned model, the data indicative of user input;
obtaining output data, from the custom trained machine-learned model, indicative of one or more content item components;
transmitting data which causes the one or more content item components to be provided for display on a user interface;
obtaining data indicative of user selection of approval of the one or more content item components; and
generating, in response to obtaining the data indicative of the user selection of the approval of the one or more content item components, one or more content items comprising a plurality of the content item components.
3. The computing system of claim 2 , wherein the content assistant component comprises one or more input fields.
4. The computing system of claim 1 , wherein the custom trained machine-learned model has been trained using a knowledge distillation training method.
5. The computing system of claim 4 , wherein the custom trained machine-learned model has been trained based at least in part on output from a pre-trained second machine-learned model.
6. The computing system of claim 5 , wherein the pre-trained second machine-learned model is a large language model.
7. The computing system of claim 6 , wherein the pre-trained second machine-learned model is tuned using one or more prompts.
8. The computing system of claim 1 , wherein the input data comprises at least one of free-form input comprising natural language input.
9. The computing system of claim 1 , wherein the input data comprises landing page content.
10. The computing system of claim 1 , wherein the one or more content item components comprises a description.
11. A computer-implemented method, performed by one or more processors, comprising:
generating an initial user interface comprising a conversational campaign assistant interface;
obtaining, via the conversational campaign assistant interface, by a custom trained machine-learned model, natural language input, wherein the natural language input comprises at least a website uniform resource locator (URL);
accessing, via the custom machine-learned model, data associated with a website associated with the website URL;
generating output comprising one or more suggested content item components based on the accessed data and natural language input,
responsive to generating the output comprising the one or more suggested content item components, automatically populating one or more input fields with the one or more suggested content item components; and
transmitting instructions that, when executed, cause a client device to provide the one or more suggested content item components for display via a user interface.
12. The computer-implemented method of claim 11 , wherein the custom trained machine-learned model has been trained using a knowledge distillation training method.
13. The computer-implemented method of claim 11 , wherein the one or more suggested content items comprise at least one of images, videos, or taglines.
14. The computer-implemented method of claim 11 , wherein generating output comprising one or more suggested content item components comprises generating a suggested content item comprising the one or more suggested content item components.
15. The computer-implemented method of claim 11 , comprising:
obtaining input comprising selection of a first content item component of the one or more content item components; and
generating, responsive to obtaining the input comprising selection of the first content item component, a content item based at least in part on the first content item component.
16. The computer-implemented method of claim 11 , comprising:
generating, based on the natural language input data, one or more follow-up questions; and
providing for display the one or more follow-up questions.
17. The computer-implemented method of claim 11 , wherein the one or more input fields comprise at least one of headlines, descriptions, or keywords.
18. One or more non-transitory computer readable media storing instructions that are executable by one or more processors to perform operations comprising:
generating an initial user interface comprising a conversational campaign assistant interface;
obtaining, via the conversational campaign assistant interface, by a custom trained machine-learned model, natural language input, wherein the natural language input comprises at least a website uniform resource locator (URL);
accessing, via the custom machine-learned model, data associated with a website associated with the website URL;
generating output comprising one or more suggested content item components based on the accessed data and natural language input;
responsive to generating the output comprising the one or more suggested content item components, automatically populating one or more input fields with the one or more suggested content item components; and
transmitting instructions that, when executed, cause a client device to provide the one or more suggested content item components for display via a user interface.
19. The one or more non-transitory computer readable media of claim 18 , the operations comprising:
generating an initial user interface comprising a content assistant component;
obtaining data indicative of user input;
processing, by the custom trained machine-learned model, the data indicative of user input;
obtaining output data, from the custom trained machine-learned model, indicative of one or more content item components;
transmitting data which causes the one or more content item components to be provided for display on a user interface;
obtaining data indicative of user selection of approval of the one or more content item components; and
generating, in response to obtaining the data indicative of the user selection of the approval of the one or more content item components, one or more content items comprising a plurality of the content item components.
20. The one or more non-transitory computer readable media of claim 18 , wherein the custom trained machine-learned model has been trained using a knowledge distillation training method.