公开(公告)号：US20240320445A1

公开(公告)日：2024-09-26

申请号：US18675840

申请日：2024-05-28

Applicant: GOOGLE LLC

Inventor： Shrestha Basu Mallick ,  Owen Lewis ,  Jaclyn Konzelmann ,  Christina Yang Choi ,  James Freedman ,  Jonathan Malmaud ,  Xin Xie ,  Brian Carver

IPC: G06F40/40

CPC classification number: G06F40/40

Abstract: Implementations described herein relate to attribution of a natural language (NL) based summary generated using a large language model (LLM). Processor(s) of a system can: receive NL based input associated with a client device, generate the NL based summary using the LLM, and process the NL based summary to determine whether a NL based summary segment of the NL based summary matches a dataset segment of a dataset that was utilized to initially train the LLM and/or to fine-tune the LLM. Further, the processor(s) can, in response to determining that the NL based summary segment matches the dataset segment, modify the NL based summary segment of the NL based summary to generate a modified NL based summary. Moreover, the processor(s) can cause the modified NL based summary to be rendered at the client device. The attribution of the NL based summary can be provided as a service to various third-parties.

公开(公告)号：US20240289395A1

公开(公告)日：2024-08-29

申请号：US18528142

申请日：2023-12-04

Applicant: Google LLC

Inventor： Hao Zhou ,  Shrestha Basu Mallick ,  Trevor Strohman ,  Patricia Luisa Romero Domingo ,  Amirhossein Kiani ,  Yu Du ,  Xinying Song ,  Heng-Tze Cheng ,  Quoc V. Le ,  Ed Huai-Hsin Chi ,  Christopher Jamie Maclean Hall

IPC: G06F16/9532 ,  G06F16/955 ,  G06F40/40

CPC classification number: G06F16/9532 ,  G06F16/955 ,  G06F40/40

Abstract: Implementations relate to helping a large language model generate factual responses to prompts that request factual content is disclosed. The large language model may receive a prompt context, a plurality of encoded context passages as input. The large language model is trained to determine whether or not to utilize the encoded context passages in generating the response. Implementations also relate to different methods of fine-tuning the responses generated by the large language model through query refinements, response re-writes, and evaluation of factual accuracy.