Conceptual Hierarchies within LLMs

While it is widely agreed that large language models (LLMs) store concepts of multiple semantic hierarchies, much remains unknown regarding the structure of this storage. The correspondence between the functional roles of LLM components and the semantic hierarchies of knowledge remains underexplored in the current literature. For example, is information organized hierarchically within layers of an LLM? We take an initial step towards causally examining the correspondence between hierarchical concepts and the multigranular structures (layers and attention heads) of various LLM models. Specifically, we generate a dataset of semantic hierarchies and investigate their storage locations in six LLMs using activation patching, a causal intervention technique. At the layer level, our findings show a moderate indication that concepts at finer levels of granularity are stored around 61-78% of the time (p < 0.01) before those at coarser granularity. There is evidence for this trend at the attention level; however, the high variability in attention level results suggests that concepts are stored across attention heads rather than within. Our results offer insight into semantic organization within LLMs.