Systems and methods for improving textual descriptions using large language models

A non-transitory computer readable storage medium storing executable instructions, execution of which by a processor causing the processor to:

• obtain, using a large language model, a textual description that includes a body of unstructured text;
• generate a rating based on the obtained textual description using a rating model, wherein the rating model comprises a machine learning model configured to output a rating based on a degree of similarity evaluated between the obtained textual description and each of a set of selected textual descriptions;
• generate, using the rating model, a suggested modification of the obtained textual description that, when applied to the obtained textual description, changes the rating of the obtained textual description, wherein the suggested modification is based on the rating output by the rating model;
• and
• output an indication of the suggested modification via a user interface,
• wherein generating the suggested modification comprises:
• identifying a feature type that is not described in the obtained textual description but that, if added to the obtained textual description, would improve the rating of the obtained textual description;
• wherein the feature type is associated with a structured data set;
• and
• wherein the suggested modification includes a recommendation to add the identified feature type to the obtained textual description.

A system comprising:

• at least one hardware processor;
• and
• at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to:
• obtain, using a large language model, a textual description that includes a body of unstructured text;
• generate a rating based on the obtained textual description using a rating model, wherein the rating model comprises a machine learning model configured to output a rating based on a degree of similarity evaluated between the obtained textual description and each of a set of selected textual descriptions;
• generate, using the rating model, a suggested modification of the obtained textual description that, when applied to the obtained textual description, changes the rating of the obtained textual description, wherein the suggested modification is based on the rating output by the rating model;
• and
• output an indication of the suggested modification via a user interface,
• wherein generating the suggested modification comprises:
• identifying a feature type that is not described in the obtained textual description but that, if added to the obtained textual description, would improve the rating of the obtained textual description;
• wherein the feature type is associated with a structured data set;
• and
• wherein the suggested modification includes a recommendation to add the identified feature type to the obtained textual description.