In recent years, the process humans adopt to learn a foreign language has moved from the strict "Grammar –Translation" method, which is based mainly on grammar and syntax rules, to more innovative processes, resulting to the more modern "Communicative approach". As its name states, this approach focuses on the coherent communication with native speakers and the cultivation of oral skills, without taking into consideration, at least at the first stages, the rules that govern the language. The same trend seems to have been applied to the way machinery can be "educated" to comprehend and reproduce the unfamiliar, human language. The "rule based" Natural Language Generation (NLG) and Natural Language Understanding (NLU) algorithms, on one hand, and the "text based" Large Language Models (LLMs), on the other, are two, analogous to the two human foreign language learning processes, subareas of Natural Language Processing (NLP). This paper presents these two alternative approaches, LLMs (a technology having surfaced as an influential catalyst of NLP, during last years) on the one hand and NLG/NLU on the other, highlighting their applications, their technologies, their capabilities, their differences, their strengths and weaknesses and the challenges they present, contributing to a deeper comprehension of the evolving landscape of Artificial Intelligence and human-computer communication.

Recommendation (recommender) systems (RS) have played a significant role in both research and industry in recent years. In the area of academia, there is a need to help researchers discover the most appropriate and relevant scientific information through recommendations. Nevertheless, we argue that there is a major gap between academic state-of-the-art RS and real-world problems. In this paper, we present a novel multi-staged RS based on clustering, graph modeling and deep learning that manages to run on a full dataset (scientific digital library) in the magnitude of millions users and items (papers). We run several tests (experiments/evaluation) as a means to find the best approach regarding the tuning of our system; so, we present and compare three versions of our RS regarding recall and NDCG metrics. The results show that a multi-staged RS that utilizes a variety of techniques and algorithms is able to face real-world problems and large academic datasets. In this way, we suggest a way to close or minimize the gap between research and industry value RS.

Large Language Models (LLMs) have been the cutting-edge technology in natural language processing (NLP) in recent years, making machine-generated text indistinguishable from human-generated text. On the other hand, “rule-based” Natural Language Generation (NLG) and Natural Language Understanding (NLU) algorithms were developed in earlier years, and they have performed well in certain areas of Natural Language Processing (NLP). Today, an arduous task that arises is how to estimate the quality of the produced text. This process depends on the aspects of text that you need to assess, varying from correct grammar and syntax to more intriguing aspects such as coherence and semantical fluency. Although the performance of LLMs is high, the challenge is whether LLMs can cooperate with rule-based NLG/NLU technology by leveraging their assets to overcome LLMs’ weak points. This paper presents the basics of these two families of technologies and the applications, strengths, and weaknesses of each approach, analyzes the different ways of evaluating a machine-generated text, and, lastly, focuses on a first-level approach of possible combinations of these two approaches to enhance performance in specific tasks.

As individuals have become overloaded with information, Recommender Systems (RS) were created to provide machine generated recommendations. Significant advancements in RS have been made thanks to Machine Learning methods; Deep Learning (DL) in particular has become extremely popular. Despite the fact that Deep neural networks (DNNs) upgrade notably the performance of RS, they make them larger and more memory-intensive systems. To that end, the solution is adding (data or model) parallel and distributed algorithms to DL RS. In this paper, we present our large-scale, multi-staged, hybrid RS that processes a million-scale dataset, as well as the most noteworthy parallel or/and distributed DL systems. Finally, we outline directions regarding the future evolution of our RS by adding some features and ideas from such systems.