R0027/2026-03-26/Q002/SRC04
LILT — Root cause analysis of non-English performance drops
Source
| Field |
Value |
| Title |
Why LLM Performance Drops in Non-English Languages |
| Publisher |
LILT |
| Author(s) |
LILT research team |
| Date |
2025 |
| URL |
https://lilt.com/blog/multilingual-llm-performance-gap-analysis |
| Type |
Industry analysis |
Summary
| Dimension |
Rating |
| Reliability |
Medium |
| Relevance |
High |
| Bias: Missing data |
Low risk |
| Bias: Measurement |
Some concerns |
| Bias: Selective reporting |
Some concerns |
| Bias: Randomization |
N/A — not an RCT |
| Bias: Protocol deviation |
N/A — not an RCT |
| Bias: COI/Funding |
Some concerns |
Rationale
| Dimension |
Rationale |
| Reliability |
Industry analysis, not peer-reviewed. But provides useful categorization of root causes with percentage attributions. |
| Relevance |
Directly identifies structural linguistic features (pro-drop, gender systems, discourse norms) as failure contributors. |
| Bias flags |
LILT is a translation company — COI in highlighting multilingual challenges. Measurement concerns about percentage attribution methodology. |
| Evidence ID |
Summary |
| SRC04-E01 |
72-87% of failures attributed to model limitations; language nuances account for ~2%; pro-drop and gender systems identified as specific structural challenges |