An intelligent correction method and system for partial hallucination of educational illustrations

Abstract

The application relates to the field of artificial intelligence and image processing technology, and provides a local hallucination intelligent correction method and system for educational illustrations, which comprises the following steps: obtaining an original illustration to be corrected and description text; generating a dynamic knowledge constraint set through a subject knowledge graph and a large language model; performing multi-dimensional hallucination detection and positioning based on the dynamic knowledge constraint set to obtain a local hallucination area and an error type; generating a region correction guide instruction based on the error type and the dynamic knowledge constraint set, correcting the local hallucination area to obtain an initial correction image; judging whether the initial correction image meets a preset convergence condition, and if not, marking the initial correction image as a new original illustration for iterative correction until the condition is met and an ultimate correction image is output. The application realizes accurate positioning and intelligent correction of local hallucination of educational illustrations, and effectively guarantees the accuracy and rigor of the correction content.