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Khalil Bibi

4 accepted papers

2024

EWEK-QA : Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems

Mohammad Dehghan, Mohammad Alomrani, Sunyam Bagga, David Alfonso-Hermelo, Khalil Bibi, Abbas Ghaddar +9
ACL 2024long

The emerging citation-based QA systems are gaining more attention especially in generative AI search applications. The importance of extracted knowledge provided to these systems is vital from both accuracy (completeness of information) and efficiency (extracting the information in a timely manner).…

Cited by 11Source ↗PDF ↗code ↗Scholar ↗
2024

Efficient Citer: Tuning Large Language Models for Enhanced Answer Quality and Verification

Marzieh Tahaei, Aref Jafari, Ahmad Rashid, David Alfonso-Hermelo, Khalil Bibi, Yimeng Wu +3
NAACL 2024findings

In recent years, there has been a growing interest in utilizing external knowledge to reduce hallucinations in large language models (LLMs) and provide them with updated information. Despite this improvement, a major challenge lies in the lack of explicit citations, which hampers the ability to veri…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2022

CILDA: Contrastive Data Augmentation Using Intermediate Layer Knowledge Distillation

Md Akmal Haidar, Mehdi Rezagholizadeh, Abbas Ghaddar, Khalil Bibi, Phillippe Langlais, Pascal Poupart
COLING 2022main

Knowledge distillation (KD) is an efficient framework for compressing large-scale pre-trained language models. Recent years have seen a surge of research aiming to improve KD by leveraging Contrastive Learning, Intermediate Layer Distillation, Data Augmentation, and Adversarial Training. In this wor…

Cited by 4Source ↗PDF ↗Scholar ↗
2022

Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Processing

Abbas Ghaddar, Yimeng Wu, Sunyam Bagga, Ahmad Rashid, Khalil Bibi, Mehdi Rezagholizadeh +8
EMNLP 2022main

There is a growing body of work in recent years to develop pre-trained language models (PLMs) for the Arabic language. This work addresses two major problems in existing Arabic PLMs that limit the progress of the Arabic NLU and NLG fields. First, existing Arabic PLMs are not well-explored and their…

Cited by 28Source ↗PDF ↗code ↗Scholar ↗