Lifted Representation Hypothesis in Language Models

arXiv:2607.19360v1 Announce Type: new Abstract: Large language models (LLMs) often answer queries by mapping individual observations to more general rule-like structures. However, it remains unclear how these structures are stored, selected, and revised. To study this process, we propose thelifted representation hypothesis: LLMs update memory through shared latent structures rather than isolated instance-level facts. This view frames lifting as an efficient use of symmetry across instances, and shattering as the refinement of coarse lifted structures into more specific subtypes. We evaluate LLMs' lifting and shattering through controlled exception-learning experiments across in-context learning, LoRA, and full fine-tuning. We find that LLMs are vulnerable to shattering failures when data are governed by nested rules and exceptions, while lifting often occurs prematurely. These results highlight the need to study the relation between data and rule structures in LLMs.

Abstract:Large language models (LLMs) often answer queries by mapping individual observations to more general rule-like structures. However, it remains unclear how these structures are stored, selected, and revised. To study this process, we propose thelifted representation hypothesis: LLMs update memory through shared latent structures rather than isolated instance-level facts. This view frames lifting as an efficient use of symmetry across instances, and shattering as the refinement of coarse lifted structures into more specific subtypes. We evaluate LLMs' lifting and shattering through controlled exception-learning experiments across in-context learning, LoRA, and full fine-tuning. We find that LLMs are vulnerable to shattering failures when data are governed by nested rules and exceptions, while lifting often occurs prematurely. These results highlight the need to study the relation between data and rule structures in LLMs.

Artificial Intelligence (cs.AI)

arXiv:2607.19360 [cs.AI]

(or arXiv:2607.19360v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.19360 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Lifted Representation Hypothesis in Language Models

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