R0027/2026-03-26/Q001 — Query Definition¶
Query as Received¶
How does prompt engineering effectiveness vary across languages? Is there published research comparing AI prompt compliance, accuracy, or reliability between English and non-English languages such as Japanese, Mandarin, Arabic, or Hindi?
Query as Clarified¶
- Subject: Prompt engineering effectiveness when applied to large language models in non-English languages
- Scope: Published empirical research comparing measurable outcomes (compliance, accuracy, reliability) of prompts in English versus non-English languages, specifically Japanese, Mandarin, Arabic, and Hindi
- Evidence basis: Peer-reviewed papers, benchmark studies, and systematic evaluations with quantified performance comparisons
- Temporal scope: Primarily 2023-2026, the period of rapid LLM capability growth
Ambiguities Identified¶
- "Prompt engineering effectiveness" is underspecified — it could mean task accuracy, instruction-following compliance, output quality, or consistency. The research will interpret this broadly as measurable performance on standardized tasks.
- The query names four specific languages but implicitly asks about non-English languages generally. The research will address both the named languages and the broader pattern.
- "Published research" could mean peer-reviewed only or include preprints and industry benchmarks. The research will include high-quality preprints (arXiv) alongside peer-reviewed venues, as this is a fast-moving field.
Sub-Questions¶
- Is there a consistent, quantified performance gap between English and non-English prompts across LLM benchmarks?
- Do the named languages (Japanese, Mandarin, Arabic, Hindi) show different magnitudes of performance degradation relative to English?
- Does the language of the prompt itself (as distinct from the language of the task data) affect model performance?
- What prompting strategies (translation, cross-lingual, native) have been empirically compared for non-English effectiveness?
Hypotheses¶
| ID | Hypothesis | Description |
|---|---|---|
| H1 | Significant, well-documented performance gap exists | Published research consistently shows measurable degradation in prompt engineering effectiveness for non-English languages compared to English |
| H2 | No meaningful or consistent gap has been demonstrated | Research either does not exist or shows no reliable performance difference across languages |
| H3 | Gap exists but is conditional on language, task, model, and prompting strategy | Performance differences are real but highly variable, depending on language resource level, task type, model architecture, and prompting approach |