General Language Research Articles

The role of artificial intelligence in higher medical education and the ethical challenges of its implementation

Artificial intelligence (AI) is penetrating higher medical education; however, its adoption remains low. A PRISMA-S search of the Web of Science database from 2020 to 2024, utilizing the search terms “artificial intelligence,” “medicine,” “education,” and “ethics,” reveals this trend. Four key areas of AI application in medical education are examined for their potential benefits: Educational support (such as personalized distance education), radiology (diagnostics), virtual reality (VR) (visualization and simulations), and generative text engines (GenText), such as ChatGPT (from the production of notes to syllabus design). However, significant ethical risks accompany AI adoption, and specific concerns are linked to each of these four areas. While AI is recognized as an important support tool in medical education, its slow integration hampers learning and diminishes student motivation, as evidenced by the challenges in implementing VR. In radiology, data-intensive training is hindered by poor connectivity, particularly affecting learners in developing countries. Ethical risks, such as bias in datasets (whether intentional or unintentional), need to be highlighted within educational programs. Students must be informed of the possible motivation behind the introduction of social and political bias in datasets, as well as the profit motive. Finally, the ethical risks accompanying the use of GenText are discussed, ranging from student reliance on instant text generation for assignments, which can hinder the development of critical thinking skills, to the potential danger of relying on AI-generated learning and treatment plans without sufficient human moderation.

Deep Learning Applications in Natural Language Processing and Optimization Strategies

In recent years, deep learning has made significant advancements in the field of natural language processing (NLP), driving the development of language understanding and generation tasks. Deep learning utilizes multi-layer neural networks to automatically extract and represent complex data features. This paper first introduces the basic principles of deep learning and mainstream frameworks (such as TensorFlow and PyTorch) and discusses core NLP tasks, including word embedding, language modeling, and text generation. Subsequently, it analyzes specific practices of deep learning applications in text classification, machine translation, automatic summarization, dialogue systems, and speech recognition. Further discussion covers model optimization methods, including structural optimization (such as RNN, LSTM, GRU, and Transformer), data preprocessing and feature engineering, hyperparameter tuning, and accelerated computation. Finally, it explores cutting-edge optimization strategies like federated learning, model compression, self-supervised learning, and transfer learning, proposing future research directions and challenges. This paper aims to systematically analyze the applications and optimization strategies of deep learning in NLP.

Systematic Analysis of Retrieval-Augmented Generation-Based LLMs for Medical Chatbot Applications

Artificial Intelligence (AI) has the potential to revolutionise the medical and healthcare sectors. AI and related technologies could significantly address some supply-and-demand challenges in the healthcare system, such as medical AI assistants, chatbots and robots. This paper focuses on tailoring LLMs to medical data utilising a Retrieval-Augmented Generation (RAG) database to evaluate their performance in a computationally resource-constrained environment. Existing studies primarily focus on fine-tuning LLMs on medical data, but this paper combines RAG and fine-tuned models and compares them against base models using RAG or only fine-tuning. Open-source LLMs (Flan-T5-Large, LLaMA-2-7B, and Mistral-7B) are fine-tuned using the medical datasets Meadow-MedQA and MedMCQA. Experiments are reported for response generation and multiple-choice question answering. The latter uses two distinct methodologies: Type A, as standard question answering via direct choice selection; and Type B, as language generation and probability confidence score generation of choices available. Results in the medical domain revealed that Fine-tuning and RAG are crucial for improved performance, and that methodology Type A outperforms Type B.

The Impact and Future of Generative Pre-trained Transformers: A Study of GPT in Enhancing Business and Technology

With the development of artificial intelligence (AI), natural language processing (NIP) has clearly become the key to enhancing AI capabilities, especially through the deployment of GPT. This paper explores the major advances and related commercial applications of GPT, as it is at the cutting edge of AI technology with far-reaching capabilities in terms of text generation, language understanding, and human-machine interaction. Developed by OpenAI, GPT uses innovative transformer architecture to make breakthroughs in a variety of language tasks, marking a major evolution in the potential applications of AI in various industries. This research explores the integration of GPT in sectors, highlighting how its complex text processing capabilities can be utilized to improve efficiency and innovative service delivery. The study highlights the transformative impact of GPT on business efficiency, strategic decision-making, and customer engagement, thanks to its scalable and adaptable AI framework. Finally, the document reflects the need for a strong ethical framework and regulatory supervision to guide the deployment of GPT technologies. As the Global Trade Agreement evolves, it is committed to shaping the future of global industry by promoting innovation while calling for a balanced approach to ethical considerations and social impacts.

Do complement clauses with first- or third-person perspective support false-belief reasoning? A training study with English-speaking 3-year-olds.

To investigate whether acquisition of the perspective-marking grammar of complement-clause constructions supports progression in children's false-belief reasoning, we conducted a training study with 76 English-speaking 3-year-olds from the North-West of England (age range: 3;0-3;10 years, 50% female, 80% White). Children were randomly assigned to one of three maximally comparable training conditions, and in a 4-week eight-session program, all children participated in the same training activities with mental-state contrasts. Depending on condition, activities were mediated linguistically with either simple clauses, first-person complements, or third-person complements. The study addressed critical confounds in previous training studies by avoiding the use of complement clauses in false-belief tests and controlling individual differences in memory, executive functioning, general language, and pretest proficiency with complement clauses. The results yielded strong support for the hypothesis of a causal influence of complement-clause exposure on false-belief progression, as children trained with first-person complements advanced significantly more in false-belief reasoning from pretest to posttest than children trained with simple clauses. Examining the roles of first- and third-person complements, a direct comparison between progression in the two complement-clause conditions showed no significant difference, but only children trained with first-person complements progressed significantly more than children in the control condition trained with simple clauses. Follow-up analyses suggested that first- and third-person complements each support false-belief progression at different stages of development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

VAN-DAMME: GPU-accelerated and symmetry-assisted quantum optimal control of multi-qubit systems

We present an open-source software package, VAN-DAMME (Versatile Approaches to Numerically Design, Accelerate, and Manipulate Magnetic Excitations), for massively-parallelized quantum optimal control (QOC) calculations of multi-qubit systems. To enable large QOC calculations, the VAN-DAMME software package utilizes symmetry-based techniques with custom GPU-enhanced algorithms. This combined approach allows for the simultaneous computation of hundreds of matrix exponential propagators that efficiently leverage the intra-GPU parallelism found in high-performance GPUs. In addition, to maximize the computational efficiency of the VAN-DAMME code, we carried out several extensive tests on data layout, computational complexity, memory requirements, and performance. These extensive analyses allowed us to develop computationally efficient approaches for evaluating complex-valued matrix exponential propagators based on Padé approximants. To assess the computational performance of our GPU-accelerated VAN-DAMME code, we carried out QOC calculations of systems containing 10 - 15 qubits, which showed that our GPU implementation is 18.4× faster than the corresponding CPU implementation. Our GPU-accelerated enhancements allow efficient calculations of multi-qubit systems, which can be used for the efficient implementation of QOC applications across multiple domains. Program summaryProgram Title: VAN-DAMMECPC Library link to program files::https://doi.org/10.17632/zcgw2n5bjf.1Licensing provisions: GNU General Public License 3Programming language: C++ and CUDANature of problem: The VAN-DAMME software package utilizes GPU-accelerated routines and new algorithmic improvements to compute optimized time-dependent magnetic fields that can drive a system from a known initial qubit configuration to a specified target state with a large (≈1) transition probability.Solution method: Quantum control, GPU acceleration, analytic gradients, matrix exponential, and gradient ascent optimization.

THE RELATIONSHIP BETWEEN MULTIPLE INTELLIGENCES AND SUCCESS IN LEARNING ENGLISH AS A FOREIGN LANGUAGE

The aim of this paper is to present the research on the relationship between multiple intelligences (MI) proposed by Gardner (1993) and success in learning English as a foreign language (EFL) among 100 grammar school students in Serbia. McKenzie’s (1999) Multiple Intelligences Survey was used in identifying grammar school students’ intelligence profiles, showing that the most prominent types of intelligence are intrapersonal, logical and kinesthetic intelligence. The results of independent samples t-test analysis indicate that gender does not affect MI or language proficiency. The results of Pearson correlation suggest that general language proficiency is positively correlated with visual and existential intelligences, while different types of productive and receptive language skills (together with grammar and vocabulary knowledge) are positively correlated with musical, existential, visual, logical and verbal intelligences. Pearson correlation results also indicate that it is not possible to develop certain types of intelligence by using teaching techniques that engage them. The research has significant educational implications, suggesting that teachers should identify their students’ intelligence profiles and adapt their teaching techniques to them, instead of trying to force the development of verbal and logical intelligences, which are, apparently wrongly, regarded as prerequisites for success in learning.Keywords: multiple intelligences, language proficiency, student-centered teaching

Development of phonetic skills of students in teaching English in a non-linguistic university

Importance. The topicality of teaching students the right pronunciation is explained by the fact that phonetic aspect is one of the components of the language competence. The problem of necessity of forming a phonetic competence of students studying a foreign language at a non-linguistic institution is considered. The aim of the study is to consider the difficulties of developing students’ phonetic skills when teaching English in a non-linguistic university, as well as the suggestion of measures to eliminate these difficulties.Materials and Methods. Within the framework of the research, referring to modern scientific sources on the problem posed, the basic difficulties have been revealed, the material related to measures on solving the difficulties of students in the development of pronunciation skills has been systematized and generalized, various measures to eliminate errors and improve pronunciation skills have been proposed.Results and Discussion. The examples of traditional phonetic exercises of varying complexity, as well as innovative ones, the use of which is supposed to improve the pronunciation skills of students, are considered; methods for assessing phonetic skills have been defined.Conclusion. Forming of phonetic competence is an important component of the general language competence of students. By means of a variety of teaching materials, active practice and creating a suitable environment, students can improve their phonetic skills and achieve a higher level of English proficiency. The practical application of the results of the research can be used in teaching English at a non-linguistic university.

A transformer-based approach to Nigerian Pidgin text generation

AbstractThis paper describes the development of a transformer-based text generation model for Nigerian Pidgin also known as Naijá, a popular language in West Africa. Despite its wide use, Nigerian Pidgin remains under-resourced, particularly in areas related to text generation and natural language processing. These difficulties are primarily due to technological constraints rather than the language’s fundamental attributes. There is currently a demand for Nigerian Pidgin-specific solutions because it is used in everyday communication and has a unique linguistic blend. This paper aims to close this gap by exploring the application of state-of-the-art transformer technology to develop a text generation model for Nigerian Pidgin. This work uses the public Afriberta-corpus dataset to optimize the Generative Pre-trained Transformer (GPT-2) model across a sizeable dataset. The performance evaluators, BLEU and Perplexity metrics provide a detailed breakdown of the model’s text quality and predictive accuracy. Despite the difficulties caused by a limited amount of training data, preliminary evaluations show that the model can generate coherent Nigerian Pidgin text. The performance evaluation yielded perplexity scores of 43.56 for variable target reference length and 43.26 for fixed text length. BLEU scores of 0.15 for fixed max length and 0.56 for variable reference target length. This highlights the quality of generated text and the significant improvement when the generated text length is aligned with the reference target. Our work was benchmarked against African American Vernacular (AAVE) revealing that BLEU scores for AAVE are significantly lower than those for Standard American English, with BLEU given as 0.26. Our Nigerian Pidgin model, with a BLEU score of 0.56, shows a better performance. However, both results suggest that both dialects are challenging for language models. Leveraging the pre-trained transformer-based language model and evaluation metrics, we showcase the model’s capacity for coherent Nigerian Pidgin text generation. For future research, the research work can serve as a good foundation for advancement and progress in the Nigerian Pidgin language generation and other low-resource languages.

The AI-empowered Researcher: Using AI-based Tools for Success in Ph.D. Programs

Generative artificial intelligence (AI) changes the picture of graduate education by providing personalized learning, automated feedback, intelligent research assistants, and automated content creation (George, 2023). AI tools will support doctoral students in text generation, language translation, responding to academic queries, and data collection and analysis and encourage self-learning and thinking development (Rasul et al., 2023; Zou & Huang, 2023). They also would be helpful for doctoral students working as teaching assistants and aiding in daily problems (Can et al., 2023; Parker et al., 2024). However, the rise of AI tools also leads to considerations of academic integrity, over-reliance on AI, misinformation, and the potential biases embedded in algorithms (George, 2023; Rasul et al., 2023). Echoing the opportunities and challenges of AI applications in research and learning, the ALISE Doctoral Students SIG wants to encourage a discussion on how doctoral students can use AI tools to empower us in the Ph.D. journey. The panel invites a diverse group of doctoral students/candidates to share how AI tools can facilitate data collection and analysis and their critical understanding of AI systems. Manar Alsaid will talk about using AI and machine learning to detect complex misinformation on social media. The talk aims to enhance our understanding of misinformation and reduce its negative impacts. This presentation will provide valuable insights for research on misinformation and information literacy. Adam Eric Berkowitz will introduce the black-box tinkering method that experimentally discerns how AI systems operate. The method enhances the transparency of AI systems, challenging the technocratic paradigm. With three examples, Berkowitz encourages attendees to learn what black-box tinkering is, how to identify cases using it, and potential opportunities to incorporate it in research. Anisah Herdiyanti will share insights from a study comparing transcripts generated by Otter.ai and Zoom Meetings. The presentation will highlight both the benefits and challenges of AI-based notes and transcription software, including technical concerns and the convenience of automated result delivery. The audience will enhance their understanding of AI tools in qualitative data transcribing and the ethical considerations in the process. Rebecca Bryant Penrose will showcase the use of HeyGen, an AI-based video generator and translation tool, in an international interview project between students at California State University Bakersfield and a Ukrainian artist/author. The presentation will increase awareness of the potential use of AI-based video and help researchers overcome language barriers in data collection. The panel will last 90 minutes, including a 5-minute introduction and a 5-minute wrap-up. Each panelist will have 10 minutes to present their topics, followed by 5-minute Q&As. A 25-minute moderated roundtable discussion will follow the panelists’ presentations to explore the potential use of different AI tools in research, including ChatGPT and AI-powered article summarizers. The panel’s learning outcomes include (1) Identifying challenges and opportunities to incorporate AI tools in research and study and (2) Explaining how to interact with AI tools to improve efficiency in research. It also provides a platform for doctoral students to share their knowledge of how AI changes research approaches and networks with each other.

Toward the Adoption of Explainable Pre-Trained Large Language Models for Classifying Human-Written and AI-Generated Sentences

Pre-trained large language models have demonstrated impressive text generation capabilities, including understanding, writing, and performing many tasks in natural language. Moreover, with time and improvements in training and text generation techniques, these models are proving efficient at generating increasingly human-like content. However, they can also be modified to generate persuasive, contextual content weaponized for malicious purposes, including disinformation and novel social engineering attacks. In this paper, we present a study on identifying human- and AI-generated content using different models. Precisely, we fine-tune different models belonging to the BERT family, an open-source version of the GPT model, ELECTRA, and XLNet, and then perform a text classification task using two different labeled datasets—the first one consisting of 25,000 sentences generated by both AI and humans and the second comprising 22,929 abstracts that are ChatGPT-generated and written by humans. Furthermore, we perform an additional phase where we submit 20 sentences generated by ChatGPT and 20 sentences randomly extracted from Wikipedia to our fine-tuned models to verify the efficiency and robustness of the latter. In order to understand the prediction of the models, we performed an explainability phase using two sentences: one generated by the AI and one written by a human. We leveraged the integrated gradients and token importance techniques, analyzing the words and subwords of the two sentences. As a result of the first experiment, we achieved an average accuracy of 99%, precision of 98%, recall of 99%, and F1-score of 99%. For the second experiment, we reached an average accuracy of 51%, precision of 50%, recall of 52%, and F1-score of 51%.

Investigating translation for Indic languages with BLOOMZ-3b through prompting and LoRA fine-tuning

In the domain of natural language processing, the rise of Large Language Models and Generative AI represents a noteworthy transition, enabling machines to understand and generate text resembling that produced by humans. This research conducts a thorough examination of this transformative technology, with a focus on its influence on machine translation. The study explores the translation landscape between English and Indic languages, which include Hindi, Kannada, Malayalam, Tamil, and Telugu. To address this, the Large Language Model, BLOOMZ-3b, is utilized, which has been primarily developed for a text generation task. Multiple prompting engineering techniques for machine translation are prominently explored. The study further traverse fine-tuning the BLOOMZ-3b model using a Parameter Efficient Fine-Tuning technique called Low Rank Adaptation, aiming to reduce computational complexity. Hence, by combining innovative prompting approaches using BLOOMZ-3b model and fine-tuning the model, it contributes to continuous development of machine translation technologies beyond traditional borders of what can be done with respect to language processing. In this regard, not only does this research shed light on the intricacy of translation problems but it also sets a precedence for optimizing or adapting big language models to various languages which end up advancing Artificial Intelligence and Natural Language Processing at large.

Dismantling the universal prosodic hierarchy with possible evidence for the absence of syllables

Abstract This commentary offers evidence for three general language types that may lack evidence for syllables as prosodic constituents: mora-dominant languages; foot-dominant languages; and word-dominant languages. It also highlights the important role of native-speaker judgements in identifying these language types.

A comparison of reserved graph grammar and edge-based graph grammar

Abstract Context-sensitive graph grammars are natural formalisms for the generation and parsing of visual programming languages. Being intuitive, rigorous, and expressive tools, they have been frequently employed in a wide range of research fields. Nevertheless, the expressive powers of the existing context-sensitive formalisms have not been investigated yet. To this end, this paper is dedicated to the comparison of expressive powers of the two prevalent graph grammar formalisms, Reserved Graph Grammar (RGG) and Edge-based Graph Grammar (EGG). By introducing a special variant of RGG as an intermediary, the relation between them concerning the expressive power is revealed that the set of graph languages of EGG is a proper subset of that of RGG. Moreover, bidirectional transformations between EGG and the variant of RGG are accordingly established, allowing them to take the advantages of each other in applications.

What should an ordinary person understand about the work of generative artificial intelligence? Proceedings of the competition "TRIZformashka-2024"

Three basic aspects of the operation of generative neural network models (generative artificial intelligence) are discussed in the article: the concept of a "token", the probabilistic nature of the generated response, and the concept of a "large model", the size of which ensures the pseudo-intelligent behavior of neural network chatbots. The issues of implementing generative models, areas and methods of their application are not discussed in principle.The materials of the "TRIZformashka-2024" competition, which was dedicated to neural network models, are provided. The fact of pseudo-intelligence of generative models is demonstrated. It turns out that a model trained on a single phrase "mama myla ramu" ("mama washed the frame") and using a context of a single letter for generation can sometimes behave as if it knows the rules of declension in the Russian language and is able to change a word by case!The concept of a "token" is considered in relation to the generation of texts, pictures and passwords. On the basis of "tokens" a practically useful method of generating passwords is built, difficult to guess, but easy to reproduce (difficult to forget).The concept of a "large model" is presented clearly and intelligibly due to its "visualization" by comparing it with physical quantities. (If one parameter of a neural network weighed one gram, then 200 freight trains would be needed to transport it. If one parameter had a length one millimeter, then the neural network would revolve around the Earth at the equator 25 times. If one second was required to learn one parameter, then it would be necessary to start training a modern neural network in the times of the Cro-Magnons.)The materials will be useful for studying generative artificial intelligence at any age.

Data Descriptor for “Understanding and Perception of Automated Text Generation among the Public: Two Surveys with Representative Samples in Germany”

With the release of ChatGPT, text-generating AI became accessible to the general public virtually overnight, and automated text generation (ATG) became the focus of public debate. Previously, however, little attention had been paid to this area of AI, resulting in a gap in the research on people’s attitudes and perceptions of this technology. Therefore, two representative surveys among the German population were conducted before (March 2022) and after (July 2023) the release of ChatGPT to investigate people’s attitudes, concepts, and knowledge on ATG in detail. This data descriptor depicts the structure of the two datasets, the measures collected, and potential analysis approaches beyond the existing research paper. Other researchers are encouraged to take up these data sets and explore them further as suggested or as they deem appropriate.

Investigating the capabilities of advanced large language models in generating patient instructions and patient educational material

ObjectivesLarge language models (LLMs) with advanced language generation capabilities have the potential to enhance patient interactions. This study evaluates the effectiveness of ChatGPT 4.0 and Gemini 1.0 Pro in providing...

Languages Generated by Conjunctive Query Fragments of FC[REG

Abstract$${\textsf{FC}}$$ FC is a finite model variant on the theory of concatenation, $${\textsf{FC}[\textsf{REG}]}$$ FC [ REG ] extends $${\textsf{FC}}$$ FC with regular constraints. This paper considers the languages generated by their conjunctive query fragments, and . We compare the expressive power of $${\textsf {FC[REG]-CQ}}$$ FC [ REG ] - CQ to that of various related language generators, such as regular expressions, patterns, and typed patterns. We then consider decision problems for $${\textsf {FC-CQ}}$$ FC - CQ and $${\textsf {FC[REG]-CQ}}$$ FC [ REG ] - CQ , and show that certain static analysis problems (such as equivalence and regularity) are undecidable. While this paper defines $${\textsf {FC-CQ}}$$ FC - CQ based on the logic $${\textsf{FC}}$$ FC , it can equally be understood as synchronized intersections of pattern languages, or as systems of restricted word equations.

Academic and general language proficiency in post-entry language assessment

Abstract In higher education systems with no or low entry requirements for domestic students, post-entry language assessments (PELAs) are often used to detect at-risk students. This study investigates how two language proficiency measures relate to students’ academic achievement: an academic reading and vocabulary (ARV) screening test, and the computer test of the Interuniversitaire Taaltest Nederlands voor Anderstaligen (ITNA), a Dutch L2-entrance test. The ARV screening test, ITNA and its language in use subtest show moderate to weak correlations with achievement. While the ARV screening test selects a large potential at-risk group; the few students failing the ITNA, language in use or scoring lower than ‘very good’ on language in use have a higher risk of underperforming. When controlling for students’ background variables, only academic language proficiency remains a significant predictor. While academic language proficiency seems more appropriate as a PELA construct, general language proficiency may identify a small, but high-risk group.

Does one size fit all? Developing an evaluation strategy to assess large language models for patient safety event report analysis.

Collecting and analyzing patient safety event (PSE) reports is a key component to the improvement of patient safety yet report analysis has been challenging. Large language models (LLMs) may support analysis; however, PSE reports tend to be a hybrid of clinical and general language. We propose a data-driven evaluation strategy to assess LLM fit for report analysis. We identify target tokens and sentences from PSE reports and use perplexity to evaluate four LLMs comprehension of the target sentence. LLMs had statistically significantly different perplexity measures in six of seven event categories. Clinical models perform better with clinical narratives, often reported by nurses and physicians. General models perform better with colloquial language and communication themes. For LLMs to support PSE report analysis there must be a good fit between the language model and the nature of the text in reports. A single LLM approach may not be the most useful strategy.