Systems and methods for language model-based text insertion
Disclosed herein are methods, systems, and computer-readable media for automatically generating and inserting text. In an embodiment, a method may include receiving an input text prompt comprising a prefix portion and a suffix portion. The method may also include accessing a language model based on the input text prompt, and determining a set of context parameters based on the input text prompt and the language model. The method may also include generating an output text prompt based on the set of context parameters and the language model, and inserting the output text prompt into the input text prompt.
1. A system comprising:
at least one memory storing instructions;
at least one processor configured to execute the instructions to perform operations for automatically generating and inserting text, the operations comprising:
receiving an input text prompt comprising a prefix portion and a suffix portion;
determining a set of model parameters based on the input text prompt;
accessing a language model based on the input text prompt and the set of model parameters;
determining a set of context parameters based on the input text prompt and the language model, the set of context parameters comprising at least one of location, person, time, or event;
generating language model output text based on the set of context parameters and the language model;
inserting the language model output text into the input text prompt based on the language model; and
optimizing the accessed language model by aligning the language model based on the language model output using machine learning,
wherein:
the language model is optimized through one or more iterative cycles of training based on one or more outcome metrics associated with the language model output and one or more datasets, and
the one or more datasets comprise at least one of user-instruction data or user-labeled data based on language model output text.
2. The system of claim 1 , wherein the input text prompt comprises computer code.
3. The system of claim 1 , wherein the prefix portion or suffix portion comprises an empty input set.
4. The system of claim 1 , wherein:
the language model is configured to identify an insertion position for the language model output text, and
the insertion position is between the prefix portion and the suffix portion.
5. The system of claim 1 , wherein the set of model parameters comprise tone, structure, or format associated with the input text prompt.
6. The system of claim 5 , wherein the set of context model parameters comprise tone, structure, and format associated with the input text prompt.
7. The system of claim 1 , wherein determining the set of context parameters comprises identifying one or more context parameters associated with the input text prompt.
8. The system of claim 1 , wherein generating the language model output text is based on the one or more context parameters associated with the input text prompt.
9. The system of claim 8 , wherein generating the language model output text-is based on a plurality of context parameters associated with the input text prompt.
10. The system of claim 1 , wherein the length of the language model output text is constrained by a length parameter of the language model, the length parameter being influenced by a user input.
11. The system of claim 1 , wherein aligning the language model based on the language model output using machine learning comprises using reinforcement machine learning.
12. The system of claim 1 , wherein inserting the language model output text is based on an insertion point defined by user input.
13. A method for automatically generating and inserting text, comprising:
receiving an input text prompt comprising a prefix portion and a suffix portion;
determining a set of model parameters based on the input text prompt;
accessing a language model based on the input text prompt and the set of model parameters;
determining a set of context parameters based on the input text prompt and the language model, the set of context parameters comprising at least one of location, person, time, or event;
generating language model output text based on the set of context parameters and the language model;
inserting the language model output text into the input text prompt based on the language mode; and
optimizing the accessed language model by aligning the language model based on the language model output using machine learning;
wherein:
the language model is optimized through one or more iterative cycles of training based on one or more outcome metrics associated with the language model output and one or more datasets, and
the one or more datasets comprise at least one of user-instruction data or user-labeled data based on language model output text.
14. The method of claim 13 , wherein the input text prompt comprises computer code.
15. The method of claim 13 , wherein the prefix portion or suffix portion comprises an empty input set.
16. The method of claim 13 , wherein:
the language model is configured to identify an insertion position for the language model output text, and:
the insertion position is between the prefix portion and the suffix portion.
17. The method of claim 13 , wherein the set of model parameters comprise tone, structure, or format associated with the input text prompt.
18. The method of claim 13 , wherein determining the set of context parameters comprises identifying one or more context parameters associated with the input text prompt.
19. The method of claim 13 , wherein generating the language model output text is based on the one or more context parameters associated with the input text prompt.
20. The method of claim 13 , wherein the length of the language model output text is constrained by a length parameter of the language model, the length parameter being influenced by a user input.