Evaluating the Performance of Large Language Models on Arabic Lexical Ambiguities: A Comparative Study with Traditional Machine Translation Systems
Bibliographic record
Abstract
The rapid advancement in natural language processing (NLP) has led to the development of large language models (LLMs) with impressive capabilities in various tasks, including machine translation. However, the effectiveness of these new systems in handling linguistic complexities, such as Arabic lexical ambiguity, remains underexplored. This study investigates whether LLMs can outperform traditional machine translation (MT) systems in translating Arabic lexical ambiguities, characterized by homonyms, heteronyms, and polysemes. The evaluation involves two prominent LLMs, OpenAI's GPT and Google's Gemini, and compares their performance with traditional MT systems, Google Translate and SYSTRAN. The results indicate that GPT and Gemini offer substantial improvements in translation accuracy and intelligibility over traditional MT systems and highlight the advanced capabilities of LLMs in handling the complexities of Arabic, suggesting a significant step forward in machine translation technologies. This study highlights the potential of LLMs to overcome the limitations of traditional MT systems and provides a foundation for future research. The results contribute to the ongoing development of more effective and accurate translation systems, emphasizing the importance of adopting advanced AI technologies in the field of machine translation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".