# Evaluating Large Language Models on Urdu Idioms

arXiv: https://arxiv.org/abs/2510.17460 · HTML: https://mlsanity.com/paper/2510.17460/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection](https://mlsanity.com/paper/2310.10482/) (2023)
- [Examining the Tip of the Iceberg: A Data Set for Idiom Translation](https://mlsanity.com/paper/1802.04681/) (2018)
- [Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting](https://mlsanity.com/paper/2310.07081/) (2023)
- [GPT-OSS-20B: A Comprehensive Deployment-Centric Analysis of OpenAI's Open-Weight Mixture of Experts Model](https://mlsanity.com/paper/2508.16700/) (2025)
- [Low-Resource Transliteration for Roman-Urdu and Urdu Using Transformer-Based Models](https://mlsanity.com/paper/2503.21530/) (2025)

## Abstract (arXiv)

Idioms remain a persistent challenge in natural language processing due to their figurative and culturally grounded meanings, which distinguish them from literal expressions. Although recent advances in large language models (LLMs) have improved idiom handling across several languages, limited attention has been given to low resource languages such as Urdu. In this work, we present a comprehensive benchmark for Urdu to English idiomatic translation, consisting of a manually verified dataset of 4,000 aligned idiom sentence pairs in both Perso Arabic (native Urdu script) and Romanized Urdu. We evaluate multiple tasks, including translation, paraphrasing, idiom span detection, and back-translation, using diverse prompting strategies such as cultural prompting, idiomatic prompting, and few-shot learning. Our findings show that frontier LLMs consistently outperform traditional neural machine translation systems across all evaluation settings, particularly in preserving figurative and metaphorical meaning. While models demonstrate relatively stable performance on native Urdu script, the absence of standardized orthography in Romanized Urdu introduces substantial challenges for consistency and idiom span detection. This work establishes a high quality benchmark for cross script idiomatic evaluation in Urdu and underscores the importance of prompt engineering in preserving figurative language meaning across languages.