Enhanced searching using fine-tuned machine learning models

1. A system comprising:

one or more hardware processors of a machine; and

at least one memory storing instructions that, when executed by the one or more hardware processors, cause the system to perform operations comprising:

receiving, by the one or more hardware processors, a search query via an interface;

accessing a pre-trained large language model designed to respond to the search query; and

performing a plurality of iterations, using the pre-trained large language model, to generate a task-specific generative model, each iteration of the plurality of iterations comprising:

performing domain-specific pre-training on an index to fine tune the pre-trained large language model;

employing the task-specific generative model to generate a search result to the search query;

analyzing the search result based on a performance metric associated with the task-specific generative model; and

refining the task-specific generative model based on the analyzing of the search result.

2. The system of claim 1 , wherein performing the plurality of iterations further comprises:

generating one or more outputs from the task-specific generative model by applying the plurality of iterations on a new search query, the one or more outputs including the search result; and

providing the one or more outputs to a user via the interface, wherein the interface is a browser-based interface.

3. The system of claim 2 , wherein performing the plurality of iterations further comprises:

receiving user feedback based on the one or more outputs; and

utilizing the user feedback to improve accuracy and fluency of the search result generated by the task-specific generative model.

4. The system of claim 1 , wherein performing the plurality of iterations further comprises:

evaluating a quality of the task-specific generative model upon conclusion of each iteration, wherein the quality includes a percentage of correct search results; and

stopping the plurality of iterations after the quality of the task-specific generative model is satisfactory to a user.

5. The system of claim 1 , the operations further comprising:

reducing a size of the task-specific generative model using asymmetric compression techniques including selective pruning of task-specific generative model parameters without identified loss of model performance.

6. The system of claim 1 , wherein performing the domain-specific pre-training on the index to fine tune the pre-trained large language model further comprises:

tailoring the pre-trained large language model using proprietary data, the proprietary data including a curated dataset representative of a plurality of types of queries and content associated with a search system.

7. The system of claim 1 , the operations further comprising:

applying a reward modeling process to the task-specific generative model to align the search result with human preferences; and

improving a quality of the search result based on the reward modeling process, wherein the reward modeling process includes collecting human annotations to define a reward function that approximates human judgments of fluency and relevance associated with the search result.

8. A method comprising:

receiving, by one or more hardware processors, a search query via an interface;

accessing a pre-trained large language model designed to respond to the search query; and

performing a plurality of iterations, using the pre-trained large language model, to generate a task-specific generative model, each iteration comprising:

performing domain-specific pre-training on an index to fine tune the pre-trained large language model;

employing the task-specific generative model to generate a search result to the search query;

analyzing the search result based on a performance metric associated with the task-specific generative model; and

refining the task-specific generative model based on the analyzing of the search result.

9. The method of claim 8 , wherein performing the plurality of iterations further comprises:

generating one or more outputs from the task-specific generative model by applying the plurality of iterations on a new search query, the one or more outputs including the search result; and

providing the one or more outputs to the user.

10. The method of claim 9 , wherein performing the plurality of iterations further comprises:

receiving, from the user, user feedback based on the one or more outputs; and

utilizing the user feedback to literately improve accuracy and fluency of the search result generated by the task-specific generative model.

11. The method of claim 8 , wherein performing the plurality of iterations further comprises:

evaluating a quality of the task-specific generative model at an end of each iteration, wherein the quality includes a percentage of correct search results; and

stopping the plurality of iterations after the quality of the task-specific generative model is satisfactory to the user.

12. The method of claim 8 , further comprising:

reducing a size of the task-specific generative model using asymmetric compression techniques including selective pruning of task-specific generative model parameters without identified loss of model performance.

13. The method of claim 8 , wherein performing the domain-specific pre-training on the index to fine tune the pre-trained large language model further comprises:

tailoring the pre-trained large language model using proprietary data, the proprietary data including a curated dataset representative of a plurality of types of queries and content associated with a search system.

14. The method of claim 8 , further comprising:

applying a reward modeling process to the task-specific generative model to align the search result with human preferences; and

improving a quality of the search result based on the reward modeling process, wherein the reward modeling process includes collecting human annotations to define a reward function that approximates human judgments of fluency and relevance associated with the search result.

15. A machine-storage medium embodying instructions that, when executed by a machine, cause the machine to perform operations comprising:

receiving, by one or more hardware processors, a search query via an interface;

accessing a pre-trained large language model designed to respond to the search query; and

performing a plurality of iterations, using the pre-trained large language model, to generate a task-specific generative model, each iteration comprising:

performing domain-specific pre-training on an index to fine tune the pre-trained large language model;

employing the task-specific generative model to generate a search result to the search query;

analyzing the search result based on a performance metric associated with the task-specific generative model; and

refining the task-specific generative model based on the analyzing of the search result.

16. The machine-storage medium of claim 15 , wherein performing the plurality of iterations further comprises:

generating one or more outputs from the task-specific generative model by applying the plurality of iterations on a new search query, the one or more outputs including the search result; and

providing the one or more outputs to a user device.

17. The machine-storage medium of claim 16 , wherein performing the plurality of iterations further comprises:

receiving user feedback based on the one or more outputs; and

utilizing the user feedback to literately improve accuracy and fluency of the search result generated by the task-specific generative model.

18. The machine-storage medium of claim 15 , wherein performing the plurality of iterations further comprises:

evaluating a quality of the task-specific generative model at an end of each iteration, wherein the quality includes a percentage of correct search results; and

stopping the plurality of iterations after the quality of the task-specific generative model is satisfactory to a user.

19. The machine-storage medium of claim 15 , wherein performing the domain-specific pre-training on the index to fine tune the pre-trained large language model further comprises:

tailoring the pre-trained large language model using proprietary data, the proprietary data including a curated dataset representative of a plurality of types of queries and content associated with a search system.

20. The machine-storage medium of claim 15 , wherein the operations comprise:

applying a reward modeling process to the task-specific generative model to align the search result with human preferences; and

improving a quality of the search result based on the reward modeling process, wherein the reward modeling process includes collecting human annotations to define a reward function that approximates human judgments of fluency and relevance associated with the search result.