Papers by Khalil Bibi
Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Processing (2022.emnlp-main)
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Abbas Ghaddar, Yimeng Wu, Sunyam Bagga, Ahmad Rashid, Khalil Bibi, Mehdi Rezagholizadeh, Chao Xing, Yasheng Wang, Xinyu Duan, Zhefeng Wang, Baoxing Huai, Xin Jiang, Qun Liu, Phillippe Langlais
| Challenge: | Existing pre-trained language models are not well-explored and are not reproducible in the literature. |
| Approach: | They propose to improve existing Arabic language pre-trained language models using a more methodical approach. |
| Outcome: | The proposed models outperform existing models on ALUE, a leaderboard-powered benchmark for Arabic NLU and NLG tasks. |
CILDA: Contrastive Data Augmentation Using Intermediate Layer Knowledge Distillation (2022.coling-1)
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Md Akmal Haidar, Mehdi Rezagholizadeh, Abbas Ghaddar, Khalil Bibi, Phillippe Langlais, Pascal Poupart
| Challenge: | Knowledge distillation (KD) is an efficient framework for compressing large-scale pre-trained language models. |
| Approach: | They propose a data augmentation technique tailored for knowledge distillation based on contrastive loss to improve masked adversarial data augmented by intermediate layer matching. |
| Outcome: | The proposed technique outperforms state-of-the-art methods on the GLUE benchmark and in an out-of domain evaluation. |
Efficient Citer: Tuning Large Language Models for Enhanced Answer Quality and Verification (2024.findings-naacl)
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Marzieh Tahaei, Aref Jafari, Ahmad Rashid, David Alfonso-Hermelo, Khalil Bibi, Yimeng Wu, Ali Ghodsi, Boxing Chen, Mehdi Rezagholizadeh
| Challenge: | Existing models with explicit citations lack the ability to verify information generated by these models. |
| Approach: | They construct a citation training dataset and fine-tune two models to address the challenge of explicit citations efficiently. |
| Outcome: | The proposed models surpass ChatGPT and exhibit exceptional out-of-domain generalization in both human and automatic evaluation. |
EWEK-QA : Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems (2024.acl-long)
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Mohammad Dehghan, Mohammad Alomrani, Sunyam Bagga, David Alfonso-Hermelo, Khalil Bibi, Abbas Ghaddar, Yingxue Zhang, Xiaoguang Li, Jianye Hao, Qun Liu, Jimmy Lin, Boxing Chen, Prasanna Parthasarathi, Mahdi Biparva, Mehdi Rezagholizadeh
| Challenge: | citation-based QA systems are suffering from two shortcomings . they usually rely only on web as a source of extracted knowledge and external knowledge sources can hamper the efficiency of the system. |
| Approach: | They propose to use a web-based knowledge graph retrieval solution to enrich extracted knowledge fed to a citation-based QA system. |
| Outcome: | The proposed model outperforms open-source state-of-the-art models in 7 quantitative and human evaluation tasks. |