Q002 — Self-Audit¶


Research	R0027 — Multilingual prompt engineering challenges
Run	2026-03-26
Query	Q002

ROBIS 4-Domain Audit¶

Domain 1: Eligibility Criteria¶

Rating: Pass

Criterion	Assessment
Evidence types defined before searching	Yes — research on linguistic structure effects, tokenization studies
Criteria stable throughout research	Yes — no shifting
Inclusion/exclusion applied consistently	Yes — 20 results dispositioned

Notes: Straightforward criteria application.

Domain 2: Search Comprehensiveness¶

Rating: Some concerns

Criterion	Assessment
Multiple search strategies used	Yes — 2 searches targeting linguistic structures and tokenization
Searches designed to test each hypothesis	Partially — searches favored H1/H3; H2-specific disconfirming evidence was harder to design searches for
All results dispositioned	Yes — 20 results, all dispositioned
Source diversity achieved	Moderate — 5 sources, mix of academic and industry

Notes: The evidence base is thinner than Q001's. Searches for SOV-specific and tonal-language-specific prompt engineering studies returned limited results, reflecting the genuine scarcity of this research.

Domain 3: Evaluation Consistency¶

Rating: Pass

Criterion	Assessment
All sources scored using same framework	Yes
Evidence typed consistently	Yes
ACH matrix applied	Yes — 6 evidence items against 3 hypotheses
Diagnosticity analysis performed	Yes

Notes: Consistent application.

Domain 4: Synthesis Fairness¶

Rating: Pass

Criterion	Assessment
All hypotheses given fair hearing	Yes — H2 was partially supported despite seeming counterintuitive
Contradictory evidence surfaced	Yes — SRC04-E01 contradicting H1 was prominently featured
Confidence calibrated to evidence	Yes — Medium confidence reflects thinner evidence base
Gaps acknowledged	Yes — missing language-specific studies noted

Notes: The synthesis gives fair weight to the surprising finding that linguistic nuances account for only ~2% of failures.

Overall Assessment¶

Overall risk of bias: Some concerns

The main concern is the thinner evidence base compared to Q001. The ~2% linguistic nuance figure from LILT is influential in the assessment but comes from a single non-peer-reviewed source. If this figure is inaccurate, it would shift the balance between H1 and H3.

Researcher Bias Check¶

Framing bias: The query assumes structural differences create "unique challenges," which could bias toward confirming their importance. The research found a more nuanced answer (challenges exist but are mediated).
Availability bias: Research on tokenization is more abundant than research on specific linguistic structural effects, potentially overstating the tokenization mechanism relative to direct linguistic effects.
Western linguistic framework bias: The categories used (SOV, tonal, inflected) are from Western linguistic typology and may not capture all relevant dimensions of how these languages interact with LLMs.