基于大语言模型的中英文整合复杂性建模研究

Integrative complexity is a concept used in psychology to measure the structure of an individuals thinking in two aspects: differentiation and integration. The measurement of integrative complexity relies primarily on manual analysis of textual content, which can be written materials, speeches, interview transcript large language models, or any other form of oral or written expression. To solve the problems of high cost of manual assessment methods, low accuracy of automated assessment methods, and the lack of Chinese text assessment scheme, this study designed an automated assessment scheme for integrative complexity on Chinese and English texts. We utilized text data enhancement technique of the large language model and the model migration technique for the assessment of integrative complexity, and explored the automated assessment methods for the two sub-structures of integrative complexity, namely, the fine integration complexity and the dialectical integration complexity. In this paper, two studies are designed and implemented. Firstly, a prediction model for the integration complexity of English text is implemented based on the text data enhancement technology of large language model; secondly, a prediction model for the integration complexity of Chinese text is implemented based on the model transfer technology. The results showed that: 1) We used GPT-3.5-Tubo for English text data enhancement, a pre-trained multilingual Roberta model for word vector extraction, and a text convolutional neural network model as a downstream model. The Spearman correlation coefficient between this models prediction of integration complexity and the manual scoring results was 0.62, with a dialectical integration complexity correlation coefficient of 0.51 and a fine integration complexity Spearman correlation coefficient of 0.60. It is superior to machine learning methods and neural network models without data enhancement. 2) In Study 2, a model with the same structure as the neural network in Study 1 was established, and the final model parameters in Study 1 were also transferred to the model in this study to train the integration complexity prediction model based on Chinese text. In the case of zero samples, the Spearman correlation coefficients of the transfer learning model for integrative complexity are 0.31, the Spearman correlation coefficient of dialectical integration complexity is 0.31, and the correlation coefficient of fine integration complexity is 0.33, all of which are better than the model in the case of random parameters (integrative complexity: 0.17, dialectical integrative complexity: 0.10, fine integrative complexity: 0.10). In the case of small samples, the Spearman correlation coefficient of the transfer learning model was 0.73, with a dialectical integration complexity correlation coefficient of 0.51 and a fine integration complexity correlation coefficient of 0.73.