Abstract
The black-box nature of Large Language Models (LLMs) necessitates novel evaluation frameworks that transcend surface-level performance metrics. This study investigates the internal neural representations of cognitive complexity using Bloom's Taxonomy as a hierarchical lens. By analyzing high-dimensional activation vectors from different LLMs, we probe whether different cognitive levels, ranging from basic recall (Remember) to abstract synthesis (Create), are linearly separable within the model's residual streams.
Our results demonstrate that linear classifiers achieve approximately 95% mean accuracy across all Bloom levels, providing strong evidence that cognitive level is encoded in a linearly accessible subspace of the model's representations. These findings provide evidence that the model resolves the cognitive difficulty of a prompt early in the forward pass, with representations becoming increasingly separable across layers.
Blogger's Review: This paper delves into the cognitive complexity of LLMs through the hierarchical lens of Bloom's Taxonomy. The findings suggest that the model can effectively distinguish between different cognitive levels early in the processing, offering critical insights into the internal mechanisms of large language models and laying the groundwork for future model optimization.