AI Research
Lifted Representation Hypothesis in Language Models
Uncovering how language models struggle to balance broad abstract rules with specific case exceptions.
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: LL...
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ArXiv CS.AI
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ArXiv CS.AI
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This research reveals that LLMs struggle with hierarchical rule refinement, often defaulting to oversimplified abstractions instead of nuanced exception handling. Understanding this lifting mechanism is critical for improving how models generalize across complex, rule-based domains without losing accuracy.
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