Abstract: Intent recognition is described. Obtained data to be recognized is preprocessed to obtain target data. Feature extraction processing is performed on the target data based on an intent recognition model to obtain a linear feature and a non-linear feature of the target data, where the intent recognition model is obtained by optimizing and training a bidirectional encoder representations from transformers (BERT) model. Intent recognition processing is performed based on the linear feature, the non-linear feature, and the intent recognition model to obtain an intent recognition result of the obtained data to be recognized.

Abstract: Embodiments of this specification disclose a method and an apparatus for identity authentication, a storage medium, and an electronic device.

Abstract: A question generation model training method, includes: obtaining a historical complex question, an answer corresponding to the historical complex question, and a historical document-based knowledge base corresponding to the historical complex question, wherein the historical document-based knowledge base includes at least one historical document related to the historical complex question; extracting an entity term related to the historical complex question from the historical document, constructing a simple question based on the entity term, and determining a plurality of historical knowledge points based on the simple question and the historical complex question; and training a question generation model based on the historical document and the plurality of historical knowledge points by using a preset loss function, to obtain a trained question generation model, wherein the question generation model is used to generate a historical complex question corresponding to the historical document.

Abstract: A system and method for generating ultimate reason codes for computer models is provided. The system for generating ultimate reason codes for computer models comprising a computer system for receiving a data set, and an ultimate reason code generation engine stored on the computer system which, when executed by the computer system, causes the computer system to train a base model with a plurality of reason codes, wherein each reason code includes one or more variables, each of which belongs to only one reason code, train a subsequent model using a subset of the plurality of reason codes, determine whether a high score exists in the base model, determine a scored difference if a high score exists in the base model, and designate a reason code having a largest drop of score as an ultimate reason code.

Abstract: Implementations of the present specification disclose an event risk detection method, apparatus, and device.

Abstract: The specification provides a method and a system for collaboratively updating a model by multiple parties for implementing privacy protection. A server can deliver an aggregation result of a t-th round of common samples to each participant i. Each participant i performs first update on a local ith model according to the t-th round of common samples and the aggregation result. Each participant i performs second update on the ith model obtained after the first update based on a first private sample fixed in a local sample set and a sample label thereof. Each participant i inputs a (t+1)th round of common samples that are used for a next round of iteration into the ith model obtained after the second update, and sends an output second prediction result to the server, so the server aggregates n second prediction results corresponding to n participants for a next round of iteration.

Abstract: Embodiments of this specification disclose a test case generation method and apparatus, a storage medium, and an electronic device. First, evaluation seed data is obtained; and then, at least one induced attack technique is designed and selected with reference to a trained generative large model, a diversified test case set is generated by performing transformation processing on the evaluation seed data, and a case label of each test case in the test case set is automatically generated.

Abstract: Implementations of the present specification provide a joint training method and apparatus for watermark embedding and detection, a storage medium, and a device.

Abstract: Embodiments of this specification disclose model authenticity evaluation methods, apparatuses, and devices. The method includes: obtaining first question data used to perform authenticity evaluation on a target model, and inputting the first question data to a target model to obtain a first response result corresponding to the first question data; extracting a named entity included in the first question data, and constructing second question data based on the named entity and the first question data, where the second question data is used to trigger the target model to output an analysis basis and a result for the first question data; inputting the second question data to the target model to obtain a model prediction result corresponding to the second question data; and determining an authenticity evaluation result of the target model based on the first response result and the model prediction result.

Abstract: Embodiments of this specification provide federated machine learning-based model training methods and apparatuses. At least two clients and at least one cloud server participate in federated machine learning-based model training. In each round of training, a first client receives a global model delivered by the cloud server; the first client obtains, through training, a gradient of the global model by using local private data; the first client encrypts the gradient obtained in the current round of training, and then sends an encrypted gradient to the cloud server; and the first client performs a next round of training until the global model converges.

Abstract: This specification discloses model optimization methods and apparatuses, devices, and storage media. A model with a low service result accuracy rate can be selected from service models as a target model, and therefore fitting can be performed based on input feature data of the target model and an output result of the target model. Therefore, a weight value corresponding to each feature dimension of the feature data input into the target model can be determined. Further, data of specific feature dimensions of the feature data that are more concerned by the target model can be determined based on the determined weight value corresponding to each feature dimension of the feature data, and the target model is optimized based on feature dimensions concerned by the target model.

Abstract: Embodiments of this specification provide a method and an apparatus for evaluating robustness of a service forecasting model, and a computing device.

Abstract: A system and method for generating ultimate reason codes for computer models is provided. The system for generating ultimate reason codes for computer models comprising a computer system for receiving a data set, and an ultimate reason code generation engine stored on the computer system which, when executed by the computer system, causes the computer system to train a base model with a plurality of reason codes, wherein each reason code includes one or more variables, each of which belongs to only one reason code, train a subsequent model using a subset of the plurality of reason codes, determine whether a high score exists in the base model, determine a scored difference if a high score exists in the base model, and designate a reason code having a largest drop of score as an ultimate reason code.

Abstract: Described is large model federated learning applied to a server. For each participating client device, an incremental parameter is sent by the client device after the client device trains a target large model of the client device, where a model parameter of the client device includes an original parameter and an incremental parameter, a magnitude of the incremental parameter is less than a magnitude of the original parameter, the original parameter remains unchanged, and the incremental parameter changes. The incremental parameter of the client device is aggregated by using incremental parameters of all client devices to obtain an aggregation parameter returned to the client device and used to update the incremental parameter of the client device. Based on the original parameter and an updated incremental parameter, redetermining a model parameter, used until target large model convergence in retraining the target large model.

Abstract: This specification discloses methods and apparatuses for model training and a task execution. An example model training method includes: obtaining behavior sequence data; converting event data of each behavior event included in the obtained behavior sequence data into text data, to obtain behavior text data corresponding to the behavior sequence data; then, inputting the obtained behavior text data into a to-be-trained recognition model, so that the to-be-trained recognition model outputs a recognition result for the behavior sequence data as a to-be-verified result based on the input behavior text data; and training the to-be-trained recognition model with an optimization objective of minimizing a deviation between the to-be-verified result output by the recognition model and an actual recognition result corresponding to the behavior sequence data.

Abstract: Multi-task model training is described. Obtaining a trained multi-task model, where a parameter includes a basic parameter and a first branch parameter. Receiving an instruction for detecting output data of a question answering task branch. Adding a task detection branch to the multi-task model and obtaining a training corpus set. Training text is input into the multi-task model. Data corresponding to the first branch parameter of the question answering task branch is masked when data are generated at each neural network layer in the multi-task model and transferred to a next neural network layer to obtain a prediction result. A second branch parameter of the task detection branch is adjusted based on the label and the prediction result that corresponds to the training text to obtain a trained multi-task model.

Abstract: The specification provides a method and a system for collaboratively updating a model by multiple parties for implementing privacy protection. A server can deliver an aggregation result of a t-th round of common samples to each participant i. Each participant i performs first update on a local ith model according to the t-th round of common samples and the aggregation result. Each participant i performs second update on the ith model obtained after the first update based on a first private sample fixed in a local sample set and a sample label thereof. Each participant i inputs a (t+1)th round of common samples that are used for a next round of iteration into the ith model obtained after the second update, and sends an output second prediction result to the server, so the server aggregates n second prediction results corresponding to n participants for a next round of iteration.

Abstract: The present specification discloses a model training method and apparatus, a service processing method and apparatus, a storage medium, and a device.