AGENTIC-SYSTEMSJuly 23, 20261 min read

Lifted Representation Hypothesis in Language Models

A new study proposes the lifted representation hypothesis to explain how large language models store and update memory, revealing that LLMs often generalize prematurely and struggle with nested rules and exceptions.

Computer Science > Artificial Intelligence

Title:Lifted Representation Hypothesis in Language Models

View PDF HTML (experimental)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.

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Source: arXiv cs.AI Recent

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