R0027/2026-03-26/Q001/SRC01
Vatsal et al. — Comprehensive survey of multilingual prompt engineering
Source
| Field |
Value |
| Title |
Multilingual Prompt Engineering in Large Language Models: A Survey Across NLP Tasks |
| Publisher |
arXiv |
| Author(s) |
Shubham Vatsal, Harsh Dubey, Aditi Singh |
| Date |
2025-05-16 |
| URL |
https://arxiv.org/abs/2505.11665 |
| Type |
Survey / review paper |
Summary
| Dimension |
Rating |
| Reliability |
Medium-High |
| Relevance |
High |
| Bias: Missing data |
Low risk |
| Bias: Measurement |
N/A |
| Bias: Selective reporting |
Some concerns |
| Bias: Randomization |
N/A — not an RCT |
| Bias: Protocol deviation |
N/A — not an RCT |
| Bias: COI/Funding |
Low risk |
Rationale
| Dimension |
Rationale |
| Reliability |
Comprehensive survey covering 36 papers, 39 techniques, 30 tasks, ~250 languages. Preprint not yet peer-reviewed, but methodology is transparent. |
| Relevance |
Directly addresses the core question of how prompt engineering varies across languages. Most comprehensive single source found. |
| Bias flags |
Some concerns about selective reporting — survey scope decisions could bias which techniques receive coverage. No specific performance comparison data reported (defers to individual papers). |
| Evidence ID |
Summary |
| SRC01-E01 |
Survey scope: 36 papers, 39 techniques, 250 languages confirming substantial research activity |
| SRC01-E02 |
Finding that native-language prompts outperform English in specific tasks |