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    Wide and deep machine learning models
    2.
    发明授权
    Wide and deep machine learning models 有权

    公开(公告)号：US10762422B2

    公开(公告)日：2020-09-01

    申请号：US15394668

    申请日：2016-12-29

    Applicant: Google LLC

    Inventor： Tal Shaked ,  Rohan Anil ,  Hrishikesh Balkrishna Aradhye ,  Mustafa Ispir ,  Glen Anderson ,  Wei Chai ,  Mehmet Levent Koc ,  Jeremiah Harmsen ,  Xiaobing Liu ,  Gregory Sean Corrado ,  Tushar Deepak Chandra ,  Heng-Tze Cheng

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.

    PROMPT COMPLEXITY FOR LARGE LANGUAGE MODELS
    3.
    发明申请
    PROMPT COMPLEXITY FOR LARGE LANGUAGE MODELS 有权

    公开(公告)号：US20250086405A1

    公开(公告)日：2025-03-13

    申请号：US18481803

    申请日：2023-10-05

    Applicant: GOOGLE LLC

    Inventor： Swaroop Mishra ,  Ragha Kotikalapudi ,  Obaid Sarvana ,  Sahitya Potluri ,  YaGuang Li ,  Taylor Bos ,  Steven Zheng ,  Hanzhao Lin ,  Chenkai Kuang ,  Heng-Tze Cheng ,  Ed H. Chi ,  Quoc Le

    IPC: G06F40/40 ,  G06N3/08

    Abstract: Some implementations relate to generating a training and/or evaluation dataset with LLM prompts (e.g., derived from user queries) based on a prompt complexity. An input prompt, for example derived from a user query, is received. The input prompt is decomposed into a prompt tree comprising a plurality of nodes. The plurality of nodes comprise: a plurality of leaf nodes corresponding to simple sub-prompts of the input query; a plurality of branch nodes of sub-prompts each corresponding to multiple simple sub-prompts; and a root node corresponding to the input prompt. A prompt complexity is determined based on a path length of the prompt tree. The prompt complexity is compared to a threshold complexity. If the prompt complexity is above the threshold complexity, the input prompt is included in a set of training prompts and/or a set of evaluation prompts.

    REDUCING COMPUTATIONAL RESOURCE USAGE VIA TRAINING AND/OR UTILIZING LARGE LANGUAGE MODELS
    4.
    发明申请
    REDUCING COMPUTATIONAL RESOURCE USAGE VIA TRAINING AND/OR UTILIZING LARGE LANGUAGE MODELS 有权

    公开(公告)号：US20240378394A1

    公开(公告)日：2024-11-14

    申请号：US18231650

    申请日：2023-08-08

    Applicant: GOOGLE LLC

    Inventor： Ragha Kotikalapudi ,  Chen Zhu ,  Steven Zheng ,  Sahitya Potluri ,  Yu Du ,  Heng-Tze Cheng ,  Quoc Le ,  Ed H. Chi

    IPC: G06F40/40 ,  G06N3/09

    Abstract: Implementations described herein relate to using self-evaluation when utilizing a large language model (LLM) to generate a response to a natural language (NL) based input. The LLM can be used to process the NL based input to generate a plurality of responses, and to generate a critique of those responses by comparing the responses to a set of response evaluation criteria. One of the responses can then be selected based on the comparison with the set of response evaluation criteria which can vary from one NL based input to another. If the NL based input was obtained a user of a client device during an inference stage, then the selected response can be rendered for presentation to the user. If the NL based input was obtained during a training stage, then the selected response can be stored as a training instance and optionally in association with additional data.

    INSTRUCTION FOLLOWING IN LARGE LANGUAGE MODELS TO REDUCE COMPUTATIONAL RESOURCE CONSUMPTION
    8.
    发明申请
    INSTRUCTION FOLLOWING IN LARGE LANGUAGE MODELS TO REDUCE COMPUTATIONAL RESOURCE CONSUMPTION 有权

    公开(公告)号：US20240394471A1

    公开(公告)日：2024-11-28

    申请号：US18231586

    申请日：2023-08-08

    Applicant: GOOGLE LLC

    Inventor： Ragha Kotikalapudi ,  Swaroop Mishra ,  Sahitya Potluri ,  Taylor Bos ,  Yu Du ,  Chen Zhu ,  Steven Zheng ,  Hanzhao Lin ,  Summer Yue ,  Heng-Tze Cheng ,  Quoc Le ,  Ed H. Chi

    IPC: G06F40/20

    Abstract: Implementations relate to improving instruction following capabilities of large language models (LLMs) using instruction decomposition, self-evaluation, and optionally progressive refinement. Processor(s) of a system can: obtain natural language (NL) based input, generate a plurality of candidate responses and evaluate the candidate responses based on instructions included in the NL based input, using an LLM, and progressively refine the candidate responses until it is determined that one or more termination criteria are satisfied. In some implementations, the NL based input can be received from a client device. In these implementations, a given candidate response that is progressively refined can be rendered for presentation at the client device and responsive to the NL base input. In additional or alternative implementations, the NL based input can be obtained from database(s). In these implementations, a given candidate response that is progressively refined can be utilized in fine-tuning of the LLM.

    QUERY RESPONSE USING A CUSTOM CORPUS
    9.
    发明公开
    QUERY RESPONSE USING A CUSTOM CORPUS 审中-公开

    公开(公告)号：US20240362093A1

    公开(公告)日：2024-10-31

    申请号：US18231606

    申请日：2023-08-08

    Applicant: GOOGLE LLC

    Inventor： Hao Zhou ,  Jamie Hall ,  Xinying Song ,  Sahitya Potluri ,  Yu Du ,  Heng-Tze Cheng ,  Quoc Le ,  Ed H. Chi

    IPC: G06F9/54 ,  G06F16/242

    CPC classification number: G06F9/547 ,  G06F16/243

    Abstract: At least utilizing a custom corpus of documents to condition a large language model (LLM) when generating a response to a user query. In some implementations, a user query associated with a client device is received. An API query for an external application is generated by an LLM based on the user query. The external application has access to a custom corpus of documents comprising a plurality of documents. The external application is queried using the API query. Data representative of one or more documents in the custom corpus of documents is received from the external application in response to the API query. The LLM generates a response to the query that is conditioned on the data representing one or more of the documents in the custom corpus of documents received from the external application. The response to the user query is caused to be rendered on the client device.

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