E01¶


Research	R0027 — Multilingual prompt engineering challenges
Run	2026-03-26
Query	Q002
Source	SRC02
Evidence	SRC02-E01
Type	Statistical

Arabic morphological complexity defeats even Arabic-centric models

URL: https://arxiv.org/html/2409.07054v1

Extract¶

Jais-13b-chat, an Arabic-centric model, "showed best results with non-native prompts and struggled significantly with native Arabic instructions." This suggests that Arabic's morphological complexity (trilateral root system, extensive derivational morphology, gender/number agreement) creates processing challenges that are not resolved even by Arabic-focused training.

Relevance to Hypotheses¶

Hypothesis	Relationship	Strength
H1	Supports	Arabic's inflectional structure creates measurable prompt engineering challenges
H2	Contradicts	Even targeted training cannot fully overcome structural challenges
H3	Supports	The challenge is mediated through tokenization and training; larger models (GPT-4o) show smaller gaps

Context¶

Arabic is a highly inflected Semitic language with a trilateral root system. Words are formed by inserting vowel patterns into consonantal roots, creating rich morphological variation. This makes tokenization particularly challenging as the same root can produce dozens of surface forms.