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Does LLM grammatical performance decline with structural complexity?

This explores whether LLMs fail uniformly at grammar or whether their failures follow a predictable pattern tied to input complexity. Understanding the relationship matters for deciding when LLM annotations are reliable.

Synthesis note · 2026-02-21 · sourced from Discourses

The finding from the LLM linguistic blind spots study is not simply "LLMs are bad at grammar." It is more precise: performance degrades as a function of structural complexity. Simple cases (single-clause sentences, surface noun identification) may be handled well. Complex cases (embedded clauses, recursive structures, complex nominals that look like clauses) fail systematically.

This is a useful calibration for practitioners because it makes failures predictable. You can audit task complexity before deciding whether to trust LLM annotation output. If the task involves syntactically simple inputs with explicit structural markers, LLM performance may be acceptable. If inputs contain embedded clauses, recursive modification, or other depth-increasing structures, expect systematic errors.

The inverse correlation between structural complexity and performance also has theoretical significance: it suggests that what LLMs learned from training data is more like a frequency-weighted surface heuristic than a recursive structural grammar. Complex structures are rare in training corpora, so the heuristics generalize poorly to them. The model can get the easy cases right without having internalized the underlying rule.

The practical design implication: for any application where structural correctness matters, build complexity-stratified evaluation sets. Testing only on typical (simple) inputs overestimates competence. The failure mode is in the structural tail.

Entailment reasoning extends this pattern to a new domain. Why do embedding contexts confuse LLM entailment predictions? identifies a specific structural complexity type: when premises are embedded under presupposition triggers (factive verbs, temporal clauses) or non-factive verbs, LLMs cannot discriminate the opposite effects these contexts should produce. The structural packaging overwhelms the semantic content. This is a direct instantiation of the complexity-degradation pattern: embedding contexts add structural depth, and LLMs respond to the embedding verb as a surface cue rather than computing its effect on the embedded content's entailment relations.

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Do LLMs rely on surface heuristics instead of learning recursive grammar rules?How do embedding contexts like presupposition triggers affect LLM entailment reasoning?Can complexity-stratified testing reveal whether LLMs understand grammatical structure?Why do rare complex structures in training data harm LLM generalization?
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llm grammatical competence degrades predictably as input structural complexity increases