Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach
This research addresses the need for cognitive assessment of LLMs to inform their development and utilization, though it is incremental as it applies existing educational diagnostic methods to a new context.
The paper tackled the problem of understanding the cognitive knowledge structure of large language models (LLMs) by evaluating them using MoocRadar, a human test dataset annotated with Bloom Taxonomy, revealing insights into their cognitive capabilities and disparate patterns.
Large Language Models (LLMs) have not only exhibited exceptional performance across various tasks, but also demonstrated sparks of intelligence. Recent studies have focused on assessing their capabilities on human exams and revealed their impressive competence in different domains. However, cognitive research on the overall knowledge structure of LLMs is still lacking. In this paper, based on educational diagnostic assessment method, we conduct an evaluation using MoocRadar, a meticulously annotated human test dataset based on Bloom Taxonomy. We aim to reveal the knowledge structures of LLMs and gain insights of their cognitive capabilities. This research emphasizes the significance of investigating LLMs' knowledge and understanding the disparate cognitive patterns of LLMs. By shedding light on models' knowledge, researchers can advance development and utilization of LLMs in a more informed and effective manner.