We explore intrinsic dimension (ID) of LLM representations as a marker of linguistic complexity. Specifically, we test whether ID differences across model layers reflect well-known complexity contrasts established in (psycho)linguistics: coordination vs. subordination, right-branching vs. center-embedding, and unambiguous vs. ambiguous attachment. Our results on six different LLMs show that these contrasts are consistently reflected in ID differences, with more complex phenomena eliciting higher ID profiles. Notably, ID differences emerge at different points across layers for different contrasts, also reaching their peaks at different stages. Further experiments using representational similarity and layer pruning confirm the trends. We conclude that ID is a useful marker of linguistic complexity in LLMs, that it points to similar linguistic processing steps across disparate LLMs, and that it has the potential to differentiate between different types of complexity.
Marco Baroni, Emily Cheng, Iria deDios-Flores et al.· arXiv.org· 3 citations
Modeling language use as a joint distribution over meanings, contexts, and utterances, upper bounds are derived on the probability that a decoder recovers a speaker's intended meaning from a representation of the utterance.
Emily Cheng, Ryan Cotterell· 0 citations
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