Papers by Hamada Zahera
LOLA – An Open-Source Massively Multilingual Large Language Model (2025.coling-main)
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Nikit Srivastava, Denis Kuchelev, Tatiana Moteu Ngoli, Kshitij Shetty, Michael Roeder, Hamada Zahera, Diego Moussallem, Axel-Cyrille Ngonga Ngomo
| Challenge: | Using a sparse Mixture-of-Experts Transformer architecture, our model is highly efficient and efficient across languages. |
| Approach: | They propose a multilingual large language model trained on more than 160 languages using a sparse Mixture-of-Experts Transformer architecture. |
| Outcome: | The proposed model performs well on natural language generation and understanding tasks while avoiding the common pitfalls of multilinguality. |
Error Analysis of Multilingual Language Models in Machine Translation: A Case Study of English-Amharic Translation (2024.emnlp-main)
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| Challenge: | Multilingual large language models have significantly advanced machine translation, yet challenges remain for low-resource languages like Amharic. |
| Approach: | They evaluated the performance of NLLB-200 and M2M in English-Amharic bidirectional translation using the Lesan AI dataset. |
| Outcome: | The proposed models outperformed the existing models in English-Amharic bidirectional translation using the Lesan AI dataset. |
Contextual Augmentation for Entity Linking using Large Language Models (2025.coling-main)
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| Challenge: | Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph. |
| Approach: | They propose a fine-tuned model that integrates entity recognition and disambiguation in a unified framework. |
| Outcome: | The proposed model achieves state-of-the-art on out-of domain datasets and compares with baselines. |