Gender Issues between Gemini and ChatGPT: The Case of English-Arabic Translation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The study focuses on the gender-related issues that face English-Arabic machine translation. It aims to investigate and evaluate gender accuracy in the translations provided by two prominent large language models, Gemini and ChatGPT, recognizing the rich morphological system of Arabic that includes gender marking. The researchers develops a test suite to evaluate gender accuracy in the translation outputs of Gemini and ChatGPT. The evaluation is performed by two professional annotators. That is followed by an analysis of the patterns of the gender-related issues that appear in the translation outputs of the models under study. The results show that Gemini outperformed ChatGPT in almost every aspect when it comes to gender-related translation issues. Both the number of the annotated issues as well as the gender accuracy evaluation came in favor of Gemini. The study introduced different patterns of gender-related translation issues. It also provides recommendations for future research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it