5 papers · 1 filter
MOSAIC: Masked Objective with Selective Adaptation for In-domain Contrastive Learning
Vera Pavlova, Mohammed Makhlouf
We introduce MOSAIC (Masked Objective with Selective Adaptation for In-domain Contrastive learning), a multi-stage framework for domain adaptation of text embedding models that inc…
Multi-stage Training of Bilingual Islamic LLM for Neural Passage Retrieval
Vera Pavlova
This study examines the use of Natural Language Processing (NLP) technology within the Islamic domain, focusing on developing an Islamic neural retrieval model. By leveraging the r…
Building an Efficient Multilingual Non-Profit IR System for the Islamic Domain Leveraging Multiprocessing Design in Rust
Vera Pavlova, Mohammed Makhlouf
The widespread use of large language models (LLMs) has dramatically improved many applications of Natural Language Processing (NLP), including Information Retrieval (IR). However,…
Leveraging Domain Adaptation and Data Augmentation to Improve Qur'anic IR in English and Arabic
Vera Pavlova
In this work, we approach the problem of Qur'anic information retrieval (IR) in Arabic and English. Using the latest state-of-the-art methods in neural IR, we research what helps t…
BIOptimus: Pre-training an Optimal Biomedical Language Model with Curriculum Learning for Named Entity Recognition
Pavlova Vera, Mohammed Makhlouf
Using language models (LMs) pre-trained in a self-supervised setting on large corpora and then fine-tuning for a downstream task has helped to deal with the problem of limited labe…