arXiv:2605.26431v1 Announce Type: new Abstract: Structural probes train on Universal Dependencies (UD), which does not encode formal-syntactic abstractions such as phase boundaries or phase-internal cohesion. Whether large language models (LLMs) encode these remains an open question that UD-based probing cannot answer by construction. We evaluate structural probes on wh-movement stimuli where UD distances are invariant across conditions by design -- any non-zero effect therefore reflects structure beyond UD. The three conditions -- bare small clause, infinitival, and finite -- are ordered by t
The proliferation of Large Language Models has necessitated deeper understanding of their internal mechanisms and representational capacities.
Understanding how LLMs process and represent linguistic structure is fundamental to advancing their capabilities and ensuring their reliability in complex tasks.
This research provides a new methodology for probing LLM internal representations beyond standard syntactic dependencies, opening avenues for more advanced structural analysis.
- · AI researchers
- · NLP developers
- · Human-computer interaction designers
Improved debugging and interpretability of Large Language Models' linguistic understanding.
Development of LLMs that incorporate more sophisticated models of human-like syntactic processing.
Enhanced AI agents capable of higher-fidelity natural language comprehension and generation, bridging gaps in current linguistic ambiguity.
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