arXiv:2605.23821v1 Announce Type: cross Abstract: We propose a distributional theory of how hypernymy -- the ``is-a'' relation between general and specific concepts -- is encoded geometrically in language representations. Starting from the empirically verified assumption that words closer on the WordNet hypernym graph co-occur more often, we characterize theoretically the spectrum of the resulting embedding Gram matrix of word2vec embeddings. Under mild positivity and decay conditions on the co-occurrence kernel, we prove that the leading eigenvectors first separate broad taxonomic branches an
This research provides a theoretical foundation for understanding how hierarchical concepts are represented in language models, emerging at a time of intense focus on AI interpretability and alignment.
A strategic reader should care because deeper understanding of how emergent properties like conceptual geometry form within LLMs can lead to more robust, controllable, and efficient AI systems, potentially accelerating AI development and application across various sectors.
Our understanding of the fundamental mechanisms behind concept formation in large language models is changing, potentially leading to new methods for engineering more sophisticated AI architectures and training paradigms.
- · AI researchers
- · LLM developers
- · NLP applications
- · AI interpretability tools
- · Undifferentiated AI model architectures
- · Black-box AI development approaches
Improved understanding of conceptual hierarchies in LLMs allows for more explainable and debuggable AI.
New model architectures could emerge that explicitly encode hierarchical concept geometry, leading to more human-like reasoning in AI.
The ability to formally define and manipulate concept relationships within AI systems could accelerate progress towards Artificial General Intelligence.
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Read at arXiv cs.LG