View source for Google llc (20240428937). Instruction Prompt Tuning for Machine-Learned Models

=Instruction Prompt Tuning for Machine-Learned Models=


==Organization Name==

[[:Category:google llc|google llc]]


[[Category:google llc]]

==Inventor(s)==

[[:Category:Vivek Natarajan of Sunnyvale CA (US)|Vivek Natarajan of Sunnyvale CA (US)]][[Category:Vivek Natarajan of Sunnyvale CA (US)]]

[[:Category:Karan Singhal of Roslyn NY (US)|Karan Singhal of Roslyn NY (US)]][[Category:Karan Singhal of Roslyn NY (US)]]

[[:Category:Shekoofeh Azizi of Vancouver (CA)|Shekoofeh Azizi of Vancouver (CA)]][[Category:Shekoofeh Azizi of Vancouver (CA)]]

[[:Category:Alan Prasana Karthikesalingam of London (GB)|Alan Prasana Karthikesalingam of London (GB)]][[Category:Alan Prasana Karthikesalingam of London (GB)]]

[[:Category:Tao Tu of New York NY (US)|Tao Tu of New York NY (US)]][[Category:Tao Tu of New York NY (US)]]

[[:Category:Seyedeh Sara Mahdavi of Oakville (CA)|Seyedeh Sara Mahdavi of Oakville (CA)]][[Category:Seyedeh Sara Mahdavi of Oakville (CA)]]

[[:Category:Christopher Semturs of San Mateo CA (US)|Christopher Semturs of San Mateo CA (US)]][[Category:Christopher Semturs of San Mateo CA (US)]]

==Instruction Prompt Tuning for Machine-Learned Models==


This abstract first appeared for US patent application 20240428937 titled 'Instruction Prompt Tuning for Machine-Learned Models




==Original Abstract Submitted==

an aspect of the present disclosure provides an example method comprising: receiving an input query associated with a particular task domain of a plurality of available task domains; obtaining a machine-learned prompt component and a curated prompt component, wherein the machine-learned prompt component comprises a plurality of machine-learned prompt values for the plurality of available task domains, and wherein the curated prompt component comprises a plurality of exemplar prompt values corresponding to one or more embedded natural language exemplars for the particular task domain from domain experts; and generating an output responsive to the input query by processing a combined prompt and the input query using a pre-trained machine-learned model, wherein the combined prompt comprises the machine-learned prompt component and the curated prompt component.



[[Category:G16H50/20]]
[[Category:CPC_G16H50/20]]