Abstract: A method for evaluating a large model, an electronic device and a computer readable storage medium are provided, which relate to a field of artificial intelligence technology, and in particular to fields of large models technology and deep learning technology. The method includes: evaluating a response information of each of M large language models for an input instruction based on a preset evaluation rule, so as to obtain a first evaluation information for each response information, where M is a positive integer greater than 1; evaluating, in response to the first evaluation information for the M large language models being consistent with each other, each response information in a plurality of evaluation dimensions, so as to obtain a second evaluation information for each response information; and determining an evaluation result representing a responsiveness of each large language model, according to the second evaluation information for each response information.

Abstract: The present disclosure provides catalysts for olefin polymerization comprising titanium, silicon, magnesium, phosphorus, at least one internal electron donor compound, and at least one halogen, processes for preparing the catalysts for olefin polymerization, and processes for olefin polymerization using the catalysts for olefin polymerization.

Abstract: The present disclosure provides catalysts for olefin polymerization comprising titanium, silicon, magnesium, phosphorus, at least one internal electron donor compound, and at least one halogen, processes for preparing the catalysts for olefin polymerization, and processes for olefin polymerization using the catalysts for olefin polymerization.