Papers with English tasks
Multilingual Multimodal Learning with Machine Translated Text (2022.findings-emnlp)
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| Challenge: | Currently, most vision-and-language pretraining research focuses on English tasks due to the availability of datasets. |
| Approach: | They propose a framework for machine translating English multimodal data to improve training data . they propose two metrics to prevent models from learning from low-quality translated text . |
| Outcome: | The proposed framework can be applied to any multimodal dataset and model. |
Weakly Supervised Cross-lingual Semantic Relation Classification via Knowledge Distillation (D19-1)
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| Challenge: | Existing work characterizes differences in meaning between words across languages using semantic relations . however, because of translation ambiguity, semantic relations are not always preserved by translation. |
| Approach: | They propose a cross-lingual relation classifier trained only with English examples and a bilingual dictionary to account for translation ambiguity when transferring knowledge from English to cross-linguistic settings. |
| Outcome: | The proposed model outperforms baselines that rely on bilingual embeddings or dictionaries for cross-lingual transfer and approaches fully supervised systems on English tasks. |
Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation (2025.coling-main)
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| Challenge: | Large language models (LLMs) are mainly trained on English data and struggle with low-resource languages. |
| Approach: | They propose to add a new language to Llama to improve classification accuracy for Persian tasks by aligning representations through bilingual pretraining and instruction datasets. |
| Outcome: | The proposed model performs on generation and classification tasks with no adverse impact and sometimes even improvements on English tasks. |
Large Language Models Can Not Perform Well in Understanding and Manipulating Natural Language at Both Character and Word Levels? (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) still exhibit significant deficiencies in basic language understanding and manipulation. |
| Approach: | They propose a bilingual benchmark to assess the performance of Large language models . they use a set of 15 simple text editing tasks to examine their capabilities . |
| Outcome: | The proposed benchmark aims to assess the performance of Large language models in basic language tasks. |
How Vocabulary Sharing Facilitates Multilingualism in LLaMA? (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) show strong performance on English tasks, but their performance in other languages is limited. |
| Approach: | They conducted an exhaustive analysis of the multilingual capability of LLMs by examining the performance gap before and after embedding fine-tuning across 101 languages. |
| Outcome: | The proposed model improves on the attributes of four quadrants in the model and provides actionable and efficient guidelines for tuning these languages. |
Evaluating Test-Time Scaling LLMs for Legal Reasoning: OpenAI o1, DeepSeek-R1, and Beyond (2025.findings-emnlp)
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| Challenge: | Experimental results show that Legal-R1 delivers competitive performance across diverse tasks. |
| Approach: | They propose to evaluate 12 large language models across 17 legal tasks across statutory and case-law traditions to determine their general reasoning performance. |
| Outcome: | The proposed model performs well across 17 legal tasks across statutory and case-law traditions. |
Crosslingual Generalization through Multitask Finetuning (2023.acl-long)
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Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M Saiful Bari, Sheng Shen, Zheng Xin Yong, Hailey Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Albanie, Zaid Alyafeai, Albert Webson, Edward Raff, Colin Raffel
| Challenge: | Multitask prompted finetuning (MTF) has been shown to help large language models generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused on English data and models. |
| Approach: | They apply multitask prompted finetuning to pretrained multilingual models and generate variants called BLOOMZ and mT0. |
| Outcome: | The proposed models can generalize to non-English languages that have never been seen before. |