Selective input data generation for machine learning models

A system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:

• generate, using a domain-specific language model, a plurality of text segment embeddings for a plurality of text segments of a plurality of input text documents associated with a predictive task;
• identify one or more prompt embeddings associated with the predictive task;
• generate a plurality of task-specific similarity scores for the plurality of text segments based on a comparison between the plurality of text segment embeddings and the one or more prompt embeddings;
• identify a set of task-specific text segments from the plurality of text segments based on the plurality of task-specific similarity scores;
• and
• train a target machine learning model based on the set of task-specific text segments.

One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to:

• generate, using a domain-specific language model, a plurality of text segment embeddings for a plurality of text segments of a plurality of input text documents associated with a predictive task;
• identify one or more prompt embeddings associated with the predictive task;
• generate a plurality of task-specific similarity scores for the plurality of text segments based on a comparison between the plurality of text segment embeddings and the one or more prompt embeddings;
• identify a set of task-specific text segments from the plurality of text segments based on the plurality of task-specific similarity scores;
• and
• train a target machine learning model based on the set of task-specific text segments.