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 to tackle this task more efficiently. Training retrieval
models requires a lot of data, which is difficult to obtain for training
in-domain. Therefore, we commence with training on a large amount of general
domain data and then continue training on in-domain data. To handle the lack of
in-domain data, we employed a data augmentation technique, which considerably
improved results in MRR@10 and NDCG@5 metrics, setting the state-of-the-art in
Qur'anic IR for both English and Arabic. The absence of an Islamic corpus and
domain-specific model for IR task in English motivated us to address this lack
of resources and take preliminary steps of the Islamic corpus compilation and
domain-specific language model (LM) pre-training, which helped to improve the
performance of the retrieval models that use the domain-specific LM as the
shared backbone. We examined several language models (LMs) in Arabic to select
one that efficiently deals with the Qur'anic IR task. Besides transferring
successful experiments from English to Arabic, we conducted additional
experiments with retrieval task in Arabic to amortize the scarcity of general
domain datasets used to train the retrieval models. Handling Qur'anic IR task
combining English and Arabic allowed us to enhance the comparison and share
valuable insights across models and languages.

通过最新的神经信息检索方法，我们研究了如何更高效地处理阿拉伯语和英语的古兰经信息检索问题。通过在大量通用领域数据上进行训练，然后在特定领域数据上继续训练，并采用数据增强技术，我们取得了在 MRR@10 和 NDCG@5 指标上明显改善的成果，为英语和阿拉伯语的古兰经信息检索设立了新的技术标杆。

利用领域适应和数据增强提高英语和阿拉伯语中古兰经 IR 的效果

Leveraging Domain Adaptation and Data Augmentation to Improve Qur'anic  IR in English and Arabic

This paper presents the development and evaluation of ChatHome, a
domain-specific language model (DSLM) designed for the intricate field of home
renovation. Considering the proven competencies of large language models (LLMs)
like GPT-4 and the escalating fascination with home renovation, this study
endeavors to reconcile these aspects by generating a dedicated model that can
yield high-fidelity, precise outputs relevant to the home renovation arena.
ChatHome's novelty rests on its methodology, fusing domain-adaptive pretraining
and instruction-tuning over an extensive dataset. This dataset includes
professional articles, standard documents, and web content pertinent to home
renovation. This dual-pronged strategy is designed to ensure that our model can
assimilate comprehensive domain knowledge and effectively address user
inquiries. Via thorough experimentation on diverse datasets, both universal and
domain-specific, including the freshly introduced "EvalHome" domain dataset, we
substantiate that ChatHome not only amplifies domain-specific functionalities
but also preserves its versatility.

本文介绍了 ChatHome，一种专门设计用于家庭装修领域的领域特定语言模型（DSLM）的开发和评估。通过对广泛的数据集进行域自适应预训练和指令调整的方法，ChatHome 能够生成与家庭装修相关的高保真度、精确的输出，既增强了领域特定功能，又保持了其通用性。