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    Active Selective Prediction Using Ensembles and Self-training
    1.
    发明公开
    Active Selective Prediction Using Ensembles and Self-training 审中-公开

    公开(公告)号：US20240249204A1

    公开(公告)日：2024-07-25

    申请号：US18419476

    申请日：2024-01-22

    Applicant: Google LLC

    Inventor： Jinsung Yoon ,  Jiefeng Chen ,  Sayna Ebrahimi ,  Sercan Omer Arik

    IPC: G06N20/20

    CPC classification number: G06N20/20

    Abstract: A method includes obtaining a set of unlabeled test data samples and, for each respective initial training step, determining a first average output for each unlabeled test data sample using a deep ensemble. For each round of a plurality of rounds, the method includes selecting a subset of unlabeled test data samples based on the determined first average outputs, labeling each respective unlabeled in the subset of unlabeled test data samples, fine-tuning the deep ensemble model using the subset of labeled test data samples, and determining a second average output for each unlabeled test data sample using the fine-tuned deep ensemble model. The method also includes generating, using the set of unlabeled test data samples and the determined second average outputs, a pseudo-labeled set of training data samples. The method also includes training the deep ensemble model using the pseudo-labeled set of training data samples.

    Complementary Prompting For Rehearsal-Free Continual Learning
    2.
    发明公开
    Complementary Prompting For Rehearsal-Free Continual Learning 审中-公开

    公开(公告)号：US20230274143A1

    公开(公告)日：2023-08-31

    申请号：US18173985

    申请日：2023-02-24

    Applicant: Google LLC

    Inventor： Zizhao Zhang ,  Zifeng Wang ,  Chen-Yu Lee ,  Ruoxi Sun ,  Sayna Ebrahimi ,  Xiaoqi Ren ,  Guolong Su ,  Vincent Perot ,  Tomas Pfister ,  Han Zhang

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: A method for rehearsal-free continual learning includes obtaining a set of training samples where training sample in the set of training samples is associated with a respective task of a plurality of different tasks. The method includes obtaining a task-invariant prompt representative of learned knowledge common to each respective task of the plurality of different tasks. The method includes, for each respective task of the plurality of different tasks, obtaining a respective task-specific prompt representative of learned knowledge specific to the respective task. The method includes, during each of one or more training iterations, for each respective training sample in the set of training samples, selecting the respective task-specific prompt representative of the respective task of the respective training sample and training a model using the task-invariant prompt and the selected respective task-specific prompt.

    Learning Self-Evaluation to Improve Selective Prediction in LLMs
    3.
    发明申请
    Learning Self-Evaluation to Improve Selective Prediction in LLMs 有权

    公开(公告)号：US20240428015A1

    公开(公告)日：2024-12-26

    申请号：US18386343

    申请日：2023-11-02

    Applicant: Google LLC

    Inventor： Jinsung Yoon ,  Jiefeng Chen ,  Sayna Ebrahimi ,  Sercan Omer Arik

    IPC: G06F40/40

    Abstract: Aspects of the disclosure are directed to methods, systems, and computer readable media for adaptation with self-evaluation to improve selective prediction in large language models (LLMs), generally referred to as ASPIRE. ASPIRE includes training LLMs on a portion of training data from a question answering task to learn self-evaluation, e.g., learn to distinguish whether a generated answer is correct or not. ASPIRE further includes a selection score that combines a likelihood of that generated answer is correct with a self-evaluation score for selective prediction. ASPIRE demonstrates improved selective prediction performance with less computational cost.

    Multimodal Learning from Structured and Unstructured Data
    4.
    发明申请
    Multimodal Learning from Structured and Unstructured Data 有权

    公开(公告)号：US20240386321A1

    公开(公告)日：2024-11-21

    申请号：US18639519

    申请日：2024-04-18

    Applicant: Google LLC

    Inventor： Sayna Ebrahimi ,  Yihe Dong ,  Tomas Pfister ,  Sercan Omer Arik

    IPC: G06N20/00

    Abstract: Aspects of the disclosure are directed to a multimodal processing system for processing both structured and un-structured data. Real-world data is not always consistent in form or content. The multimodal processing system includes model that can be trained to account for this characteristic of real-world data, by selectively masking data of different modalities during pretraining to learn outputs that are the same or comparable between the masked and un-masked inputs. The model is trained according to modality-specific masking objectives computed for each modality of data and joint modality similarity-based masking objectives for a joint representation of the data across all modalities. The system provides consistent and accurate input, even when input data may have substantial portions of data from different modalities missing. Cross-modal relationships in data are reinforced by the model as different portions of data are masked, contributing to an overall increase in model accuracy versus other approaches.

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