Tracing the complexity profiles of different linguistic phenomena through the intrinsic dimension of LLM representations
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
Title:Tracing the complexity profiles of different linguistic phenomena through the intrinsic dimension of LLM representations
Abstract: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.
| Comments: | Published as a conference paper at EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2601.03779 [cs.CL] |
| (or arXiv:2601.03779v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2601.03779
arXiv-issued DOI via DataCite
|
|
| Journal reference: | Proceedings of EMNLP 2026 |
Submission history
From: Marco Baroni [view email][v1] Wed, 7 Jan 2026 10:16:59 UTC (7,506 KB)
[v2] Fri, 24 Apr 2026 10:47:16 UTC (10,836 KB)
[v3] Fri, 28 Aug 2026 12:28:58 UTC (10,147 KB)
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
References & Citations
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — NLP / Computation & Language
-
Space as an Interventional Invariant: Cross-Modal Predictive Geometry for Stratified Cities and Em-Spaced Intelligence
Sep 14
-
MAxBench: A Multinomial Concept Recovery Benchmark
Sep 14
-
R2VC: Modular Fact-Checking with Retrieval, Verification, and Confidence Calibration
Sep 14
-
What Counts as a Mistake? Annotating Recitation Events in Quran Memorization Transcripts
Sep 14
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.