From Found to Designed: Concepts as a Design Axis for Large Language Models
Large language models (LLMs) encode rich concept-like information, but represent it implicitly through distributed statistical associations rather than as explicit, structured, compositional concepts. Consequently, concept-level structure is typically \emph{found} rather than \emph{designed}: it is recovered after training through probing or dictionary learning, with no architectural guarantee of stability, compositionality, controllability, or alignment with human conceptual organization. We organize concept-aware interventions along two dimensions: whether concept structure is internally induced or externally grounded, and the stage of the pipeline where it is introduced. This taxonomy ...
| Authors | Chen Shani |
|---|---|
| Primary category | cs.CL |
| Published | 29 July 2026 |
| Last updated at source | 30 July 2026 |
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