Natural Language Processing (NLP) and Artificial Intelligence (AI): Prospects for Enhancing Scientific Research in the Arabic Context

Abstract

The current study identifies the evolution of Natural Language Processing (NLP) and its integration with artificial intelligence technologies to enhance scientific research. The analysis ranges from traditional rule-based systems to deep machine learning-based models, all the way to large language models (LLMs) and transformer technologies. The study discusses modern tools and technologies such as Vector Representations (Embeddings), Learning with Instructions, Reinforcement Learning with Human Feedback (RLHF), and Retrieval Augmented Generation (RAG), with the use of models in interaction with external tools to enhance performance (Bommasani et al., 202).Practical applications of the study include exploring scientific literature, summarizing texts, designing experiments, generating hypotheses, scientific writing, and translation, with prominent examples such as AlphaFold in the biological sciences. In addition, the study highlights key challenges, including generating erroneous model content, bias, privacy protection, source reliability, and reproducibility. In this context, the study proposes a practical framework for ethical and responsible governance of Natural Language Processing (NLP) applications in Arabic scientific research, including transparency, process documentation, human review, privacy policies, and quality assurance of results, taking into account the Arabic context and the need to develop compatible linguistic resources and legislation (Solaiman et al., 201).The study concludes that the integration of Natural Language Processing (NLP) and  Artificial Intelligence (AI) is not intended to replace human researchers, but rather to expand their capabilities to access knowledge, analyze data, and accelerate scientific research with responsibility and accuracy.