Abstract: A method and apparatus for training a question solving model, a question solving method and apparatus, an electronic device and a readable storage medium are disclosed. The method for training a question solving model includes: acquiring a first sample question; inputting the first sample question and a solving step grabbing template into a large language model to obtain a first sample solving step; inputting the first sample question, the first sample solving step and an answer grabbing template into the large language model to obtain a first sample answer; pre-training a step planning model according to the first sample question and the first sample solving step; pre-training the large language model according to the first sample question, the first sample solving step and the first sample answer; and acquiring the question solving model according to the step planning model and the large language model obtained by pre-training.

Abstract: A method is provided that includes: obtaining current dialogue data; determining a requirement type of the user in the current round of dialogue based on the current dialogue data; in response to the requirement type being an image processing requirement, determining an action sequence for implementing the image processing requirement; executing the action sequence to generate a target image; and generating response data corresponding to the user input data based on the target image.

Abstract: A large model optimization training method in the artificial intelligence fields, such as large models, deep learning, natural language processing, may include: taking, as candidate queries, queries collected from a predetermined data source and capable of serving as input to a large model in response to determining that an optimization triggering condition is met; screening out target queries from the candidate queries, the target queries being queries which cannot be correctly processed by the large model; and constructing respectively corresponding training samples according to the target queries, the training samples being used for carrying out optimization training on the large model.

Abstract: A technical solution for processing a model generation result, which relates to the field of artificial intelligence technologies is disclosed. An implementation includes: disassembling a text generation result of a generative large model to obtain a plurality of result logic units; wherein each result logic unit includes a segment in the text generation result; each segment is capable of independently identifying one premise or conclusion in a logical inference relationship of the text generation result; and the text generation result is a response result generated by the generative large model based on text input information; generating a logical inference graph capable of characterizing a logical inference relationship among the plurality of result logic units based on the plurality of result logic units; and determining whether logical inference of generation of the text generation result by the generative large model is correct or not based on the logical inference graph.

Abstract: The present disclosure provides a search method, an electronic device and a storage medium, and belongs to a technical field of the Internet. The method includes: determining a first set of features corresponding to an original search statement by parsing the original search statement; obtaining each initial search result corresponding to the original search statement; determining a second set of features corresponding to an initial search result by parsing a title of the initial search result; determining a rewritten search statement corresponding to the original search statement and the initial search result by codecing the first set of features and the second set of features; and obtaining a supplementary search result corresponding to the rewritten search statement.

Abstract: Embodiments of the application disclose a summary generation method and apparatus. A specific embodiment of the method comprises: acquiring a target article including a headline and a body of the article; determining whether a question is included in the headline; determining, in the body of the article, an information-satisfied-paragraph including an answer to the question, in response to determining that the question is included in the headline; and generating a summary of the target article based on the determined information-satisfied-paragraph. The above embodiment may generate a summary that directly satisfies the users' demand for information.

Abstract: A method is provided. The method includes converting a search request of a user into a first request semantic vector. The method further includes searching a search resource database for at least one first data semantic vector matched with the first request semantic vector, wherein the search resource database is constructed as a semantic vector space in which different types of data are converted into corresponding data semantic vectors, and the different types of data include at least texts, pictures and videos. The method further includes generating, based on the at least one first data semantic vector, a search result.

Abstract: A method and apparatus for recognizing a low-quality article based on artificial intelligence, a device and a medium. The method comprises: obtaining a user feedback behavior feature of a to-be-recognized article in a news-recommending system; according to the user feedback behavior feature of the to-be-recognized article and a predetermined low-quality article recognition model, recognizing whether the to-be-recognized article is a low-quality article.

Abstract: There is provided a method and apparatus for generating a competition commentary based on artificial intelligence, and a storage medium. The method comprises: obtaining commentator's words commentaries and structured data of historical competitions; generating a commentating model according to obtained information; during live broadcast of a competition, determining a corresponding words commentary according to the commentating model with respect to the structured data obtained each time.

Abstract: The present disclosure provides a method and apparatus for evaluating article value based on artificial intelligence, and a storage medium. The solution of present disclosure may be employed to pre-mine high-quality articles and low-quality articles as training data, and train according to the training data to obtain a value-scoring model. As such, value evaluation needs to be performed for the to-be-evaluated article, it is feasible to first perform feature extraction for the to-be-evaluated article, determine a score of the to-be-evaluated article based on the extracted features and the value-scoring model, and thereby implement effective evaluation of the article value.

Abstract: Embodiments of the present disclosure provide a method and an apparatus for training a machine reading comprehension model, and a storage medium. The method includes: training an initial model to generate an intermediate model based on sample data; extracting samples to be processed from the sample data according to a first preset rule; generating a noise text according to a preset noise generation method; adding the noise text to each of the samples to be processed respectively to generate noise samples; and performing correction training on the intermediate model based on the noise samples to generate the machine reading comprehension model.

Abstract: A model training method, a model training platform, an electronic device and a storage medium are provided, which can be used in the field of artificial intelligence, particularly the fields of natural language processing and deep learning. The model training method includes: receiving an input; determining, based on the input, a user-oriented prefabricated function; determining, based on the input, a model training function; determining, based on the input, a pre-trained model; determining, based on the input, a network structure associated with the pre-trained model so as to support use of the pre-trained model; training, based on the input, the model by using the prefabricated function, the model training function, and the pre-trained model; and providing an output associated with a trained model.

Abstract: Embodiments of the disclosure disclose a method and apparatus for pushing information based on artificial intelligence. A specific embodiment of the method includes: mining, in response to a new match occurring, real-time match data of the match and real-time associated data of the match; generating structured data using the real-time match data; generating a to-be-recommended item using the real-time associated data and an offline item; determining whether a current time point is a recommendation node based on the structured data and real-time match state information acquired from a state manager; generating a to-be-pushed message based on the to-be-recommended item, the real-time match state information, and basic match information acquired from the state manager, and updating a to-be-pushed message record in the state manager, if the current time point is the recommendation node; and pushing the to-be-pushed message. The embodiment has improved the quality and timeliness of pushing a to-be-pushed message.

Abstract: Embodiments of the present disclosure provide a method and an apparatus for training a machine reading comprehension model, and a storage medium. The method includes: training an initial model to generate an intermediate model based on sample data; extracting samples to be processed from the sample data according to a first preset rule; generating a noise text according to a preset noise generation method; adding the noise text to each of the samples to be processed respectively to generate noise samples; and performing correction training on the intermediate model based on the noise samples to generate the machine reading comprehension model.

Abstract: Embodiments of the application disclose a summary generation method and apparatus. A specific embodiment of the method comprises: acquiring a target article including a headline and a body of the article; determining whether a question is included in the headline; determining, in the body of the article, an information-satisfied-paragraph including an answer to the question, in response to determining that the question is included in the headline; and generating a summary of the target article based on the determined information-satisfied-paragraph. The above embodiment may generate a summary that directly satisfies the users' demand for information.

Abstract: The present disclosure provides a method and apparatus for recognizing a low-quality news resource, a computer device and a readable medium. The method comprises: obtaining information of a to-be-recognized news resource; determining whether the to-be-recognized new resource is a low-quality news resource, according to a pre-built low-quality news information repository and the information of the to-be-recognized news resource. The technical solutions of the present disclosure may be employed to automatically recognize whether the to-be-recognized news resource is the low-quality resource through the information of the to-be-recognized news resource such as the text information and/or pictures, and can overcome technical problems in the prior art such as time-consuming and arduous manual-check manner and a low recognition efficiency due to leakage in checking.

Abstract: The present disclosure provides a search method, an electronic device and a storage medium, and belongs to a technical field of the Internet. The method includes: determining a first set of features corresponding to an original search statement by parsing the original search statement; obtaining each initial search result corresponding to the original search statement; determining a second set of features corresponding to an initial search result by parsing a title of the initial search result; determining a rewritten search statement corresponding to the original search statement and the initial search result by codecing the first set of features and the second set of features; and obtaining a supplementary search result corresponding to the rewritten search statement.

Abstract: Embodiments of the present disclosure provide a method and a device for determining a comment, a server and a storage medium. The method includes: obtaining text content of an event; determining an event tag of the event according to the text content; comparing the event tag with comment tags in a comment graph, to determine candidate comments, the comment graph being generated according to other events and corresponding comments, and the comment graph including comments and corresponding comment tags; and screening the candidate comments, to determine a candidate comment satisfying a preset condition as the comment of the event.

Abstract: Embodiments of the present disclosure provide a method and a device for managing a dialogue based on artificial intelligence. The method includes the followings. An optimum system action is determined from at least one candidate system action according to a current dialogue status feature, a candidate system action feature and surrounding feedback information of the at least one candidate system action and based on a decision model. Since the current dialogue status corresponding to the current dialogue status feature includes uncertain results of natural language understanding, the at least one candidate system action acquired according to the current dialogue status also includes the uncertain results of natural language understanding.

Abstract: Embodiments of the disclosure disclose a method and apparatus for pushing information based on artificial intelligence. A specific embodiment of the method includes: mining, in response to a new match occurring, real-time match data of the match and real-time associated data of the match; generating structured data using the real-time match data; generating a to-be-recommended item using the real-time associated data and an offline item; determining whether a current time point is a recommendation node based on the structured data and real-time match state information acquired from a state manager; generating a to-be-pushed message based on the to-be-recommended item, the real-time match state information, and basic match information acquired from the state manager, and updating a to-be-pushed message record in the state manager, if the current time point is the recommendation node; and pushing the to-be-pushed message. The embodiment has improved the quality and timeliness of pushing a to-be-pushed message.