Abstract: A method for model training based on a large model includes: determining a first large model as a teacher model of a language model, and performing distillation learning on the language model based on the first large model; inputting a first prompt text into the language model, and obtaining a plurality of first response texts for the first prompt text output by the language model; determining a reference response text for the first prompt text from the plurality of first response texts; and training the language model based on the reference response text for the first prompt text.

Abstract: A method for generating a text training sample based on a large model includes: obtaining at least two query clusters by clustering at least two queries; obtaining a first query from each query cluster; generating at least two second queries under a set theme through a first large model by taking the first query as an example; and generating a first text training sample for fine-tuning a second large model based on the second query.

Abstract: A computer-implemented method for information processing based on a large language model is provided. The method includes obtaining query information provided by a user. The method further includes determining memory information related to the query information. The method further includes determining, based on the query information and the memory information, a tool for processing the query information. The method further includes invoking the tool to obtain auxiliary information. The method further includes generating, based on the query information and the auxiliary information, a result of processing the query information.

Abstract: An apparatus for training a large language model includes: at least one sample text instruction is input into a target large language model to obtain at least one standard response text, and the at least one sample text instruction is input into a large language model to be trained to obtain at least one predicted response text. A first sample response text is determined from the at least one standard response text according to the score difference between a first quality score of a standard response text and a second quality score of a predicted response text. A first target training sample is generated according to the first sample response text and a sample text instruction corresponding to the first sample response text, and a training dataset is constructed according to the first target training sample.

Abstract: The disclosure provides a method for training a tag recommendation model. The method includes: collecting training materials that comprise interest tags in response to receiving an instruction for collecting training materials; obtaining training semantic vectors that comprise the interest tags by representing features of the training materials using a semantic enhanced representation frame; obtaining training encoding vectors by aggregating social networks into the training semantic vectors; and obtaining a tag recommendation model by training a double-layer neural network structure using the training encoding vectors as inputs and the interest tags as outputs. Therefore, the interest tags obtained in the disclosure are more accurate.

Abstract: A method for extracting information, includes: obtaining an information stream comprising text and an image; generating, according to the text, embedded representations of textual entity mentions and a textual similarity matrix of the textual entity mentions and candidate textual entities; generating, according to the image, embedded representations of image entity mentions and an image similarity matrix of the image entity mentions and candidate image entities; and determining, based on an optimal transport, target textual entities of the textual entity mentions and target image entities of the image entity mentions according to the embedded representations of the textual entity mentions, the embedded representations of the image entity mentions, the textual similarity matrix and the image similarity matrix.

Abstract: The disclosure provides a method for recognizing an entity, an electronic device and a storage medium. The method includes: obtaining message data to be processed; obtaining entity mention information by processing the message data to be processed according to a multi-pattern matching method; determining one or more candidate entities associated with the entity mention information and entity description information corresponding to the one or more candidate entities; and determining a target entity mentioned in the entity mention information according to the message data to be processed and the entity description information.