R0027/2026-03-26/Q002/H1¶
Statement¶
Each structural category (SOV word order, tonal systems, inflectional morphology) produces distinct, documented challenges for prompt engineering that are directly attributable to the linguistic structure itself.
Status¶
Current: Partially supported
H1 is partially supported. Linguistic structural features do create identifiable challenges — Japanese SOV order, Arabic morphological complexity, Finnish agglutination — but the evidence shows these challenges manifest primarily through computational mechanisms (tokenization, training data representation) rather than through the linguistic structure directly. The structural features are risk factors, not direct causes.
Supporting Evidence¶
| Evidence | Summary |
|---|---|
| SRC01-E01 | Morphology, syntax, lexico-semantics all influence prompt effectiveness |
| SRC01-E02 | Japanese needs explicit subjects; Arabic needs gender context; Finnish has 15 cases |
| SRC02-E01 | Arabic morphological complexity defeats even Arabic-centric models |
Contradicting Evidence¶
| Evidence | Summary |
|---|---|
| SRC04-E01 | Language nuances account for only ~2% of failures; model limitations (72-87%) dominate |
Reasoning¶
The evidence confirms that linguistic structure creates challenges, but the LILT root cause analysis showing only ~2% of failures attributable to language nuances directly (vs 72-87% to model limitations) significantly weakens H1's claim that the challenges are "directly attributable to linguistic structure itself."
Relationship to Other Hypotheses¶
H1 and H3 agree that challenges exist but disagree on the primary mechanism. H3 is better supported because it identifies the mediating mechanism (tokenization, training data).