Guided synthetic data generation for contextual representation

A system for synthesizing an annotated contextualized dataset for a machine learning model, comprising:

• one or more hardware processors;
• an annotation identifier stored in memory and executable by the one or more hardware processors and configured to perform operations comprising selecting one or more first deterministic annotations from a set of candidate deterministic annotations, wherein the set of candidate deterministic annotations correspond to a first data type identified in accordance with annotation selection rules;
• a datapoints generator stored in memory and executable by the one or more hardware processors and configured to perform operations comprising:
• generating a first synthesizing instruction for input to a synthesizing language model based on the one or more first deterministic annotations, a synthesizing instruction template, and a scenario description specifying a task of a specified domain for which the machine learning model is to be trained or evaluated;
• and
• synthesizing a first input prompt and one or more first probabilistic annotations by the synthesizing language model based on the first synthesizing instruction;
• and
• a datapoints validator stored in memory and executable by the one or more hardware processors and configured to perform operations comprising adding a first datapoint to the annotated contextualized dataset, the first datapoint including the one or more first deterministic annotations, the first input prompt, and the one or more first probabilistic annotations.

One or more tangible processor-readable storage media embodied with instructions for executing on one or more processors and circuits of a computing device a process for synthesizing an annotated contextualized dataset for a machine learning model, the process comprising:

• selecting one or more first deterministic annotations from a set of candidate deterministic annotations, wherein the set of candidate deterministic annotations correspond to a first data type identified in accordance with annotation selection rules;
• generating a first synthesizing instruction for input to a synthesizing language model based on the one or more first deterministic annotations, a synthesizing instruction template, and a scenario description specifying a task of a specified domain for which the machine learning model is to be trained or evaluated;
• synthesizing a first input prompt and one or more first probabilistic annotations by the synthesizing language model based on the first synthesizing instruction;
• and
• adding a first datapoint to the annotated contextualized dataset, the first datapoint including the one or more first deterministic annotations, the first input prompt, and the one or more first probabilistic annotations.