The article analyses how, in line with two recent literary trends—the turn to sincerity and realism in post-postmodern fiction as well as the emergence of literary works reconsidering the viability of the myth of the American Dream in the increasingly unequal US—Cameroonian-American writer Imbolo Mbue’s Behold the Dreamers (2016) provides a realist view of the country which counters the master or grand narrative of the American Dream and the meritocratic ideals that sustain it. Although previous scholarly work on the novel has focused on its exposure of the limits that the promises behind the Dream present for racialised immigrants like the protagonist Cameroonian family in Mbue’s novel, this article seeks to contribute to the discussion by exploring an overlooked dimension of this immigrant narrative set around the Great Recession of 2008. Specifically, it examines how, through a deployment of social realism with touches of naturalism, Mbue portrays the clash between her characters’ former expectations of the United States as the Promised Land of equal opportunities through hard work and the harsh reality they encounter there. This reality is nothing but a country shaped by a hyper-individualistic and competitive neoliberal economic system that became particularly predatory in the aftermath of the 2008 crash. Hence, the article contends that, behind this formal choice, lies Mbue’s aim to expose and by extension dismount the dominant albeit fallacious narrative of the US meritocratic Dream. Ultimately, the article explores the protagonist family’s deep and cruel attachment to this widespread myth until their eventual awakening in an ambiguous ending of return to the homeland which, through its detailed reflection of the harmfulness underlying these characters’ blind faith in a constructed and thus elusive dream, counters the cultural narratives promoting it.
Teaching Business English extends beyond mastering vocabulary and grammar; it equips learners with the skills to communicate effectively in professional environ-ments. In busines world, soft skills such as communication, teamwork, problem-solving, and negotiation are equally important as linguistic proficiency. This articleexamines effective methodologies for integrating soft skills into Business Englishteaching by reviewing relevant literature and case studies published between 2019and 2024. The findings highlight the urgency of incorporating soft skills into thecurriculum to enhance learners’ ability to navigate real-world business contexts.Several practical methodologies, such as project-based learning, role-playing, andcase study analysis, are recommended to support this integration. This discussionprovides valuable insights for curriculum designers, educators, and learners, help-ing them optimize their roles in fostering comprehensive Business English educa-tion.
The growing interest in critical English language education research within the Brazilian context highlights the need for a systematic review aimed at mapping the state of the art. I used Google Scholar to identify 143 articles which discussed the topic and met the inclusion criteria. The review shows that the growing prominence of critical English language education, linked to broader educational orientations and the rise of Critical Applied Linguistics in Brazil, emphasizes discussions on “critical themes”, particularly issues affecting minority groups. The review highlights a lack of research on the systematic teaching of language, though existing studies show notable progress. The findings can support educational policymakers in reimagining approaches to teaching language for social justice, grounded in a theme-based curriculum, moving away from the current model that emphasizes skills development.
Hoang Yen Phuong, Ngoc Bao Chau Tran, Thi Thuy Linh Nguyen
et al.
Abstract In Vietnamese English language classrooms, assessment has long been shaped by the weight of summative exams and the pressures of test-oriented instruction. Yet in the margins of this exam-driven culture, some teachers are beginning to reimagine assessment, not as a final judgment, but as a process embedded in learning itself. This study explores how Vietnamese EFL teachers respond to washback by adopting formative assessment (FA) practices following participation in British Council professional development (PD) programs. Drawing on a corpus of written reflections from diverse teaching contexts, the study examines how teachers reframe assessment, navigate institutional tensions, and improvise pedagogical strategies to support student learning. Using a thematic narrative approach, the analysis reveals a gradual shift from score-focused routines to more dialogic, responsive, and student-centered practices. Teachers described integrating informal games, peer feedback, and observation-based adjustments as ways of making learning visible without relying solely on tests. At the same time, they negotiated complex tensions between institutional expectations and pedagogical intentions, often making quiet, localized changes within rigid systems. The findings highlight teacher agency as a central force in shaping assessment reform and suggest that even modest acts of FA can create space for more inclusive and meaningful learning.
<p>Complex structures, which consist of dependent and independent clauses, make it difficult for Iranian high school students to recognize their grammatical complexities. This study investigates the effect of collaborative tasks (i.e., co-practice task writers, corrective feedback providers, and evaluators) on the types of corrective feedback (i.e., teacher feedback vs. peer feedback) and their impact on EFL learners' recall and production of complex structures. A quasi-experimental design was adopted, involving three equal intact classes comprising 96 lower-intermediate students, selected through convenience sampling. A production pretest and posttest of complex structures, as well as recall pretests and posttests of complex structures, were implemented following a pilot study to validate the tests. A two-way multivariate analysis of covariance (MANCOVA) was run. Findings revealed that collaborative tasks (co-practice task writers, corrective feedback providers, and evaluators) have a positive effect on high school students' recall and production of complex structures. Additionally, teacher feedback is a significant factor in students' recall and production of complex structures. No significant interaction was observed between explicit instruction of collaborative tasks and types of feedback on learners' recall and production of complex structures. The results address several suggestions for EFL teachers, learners, and pedagogical practitioners.</p>
Theory and practice of education, English language
Abstract Artificial intelligence (AI) models have demonstrated significant success in classifying various types of text. However, the complex nature of these models often complicates the interpretability of their classifications. To address these challenges and to enhance explainability, this study proposes a novel approach to text classification leveraging natural language processing (NLP) techniques and explainable AI (XAI) methods. Text preprocessing steps were essential for improving the quality of text analysis. This was gained by eliminating elements that contribute minimal semantic value. To achieve robust performance and mitigate the risk of overfitting, repeated stratified K-Fold cross-validation was utilized. Furthermore, the synthetic minority oversampling technique (SMOTE) was employed to address dataset imbalance issues. In the classification phase, nine machine learning models and hybrid/multi-model approaches were employed. To validate the explainability of the classifications, the local interpretable model-agnostic explanations (LIME) framework was utilized. The study utilized two datasets containing texts from domains such as sports, medicine, entertainment, politics, technology, and business. Empirical evaluations demonstrated the effectiveness of the proposed approach. The proposed hybrid model achieved exceptional performance across key metrics, including accuracy, precision, recall, and F1-score. The proposed hybrid model achieved results of up to 99% accuracy. This work can be used for various text analysis applications.
Electrical engineering. Electronics. Nuclear engineering, Information technology
Open English Wordnet is a key resource published in OntoLex-lemon as part of the linguistic linked open data cloud. There are, however, many links missing in the resource, and in this paper, we look at how we can establish hypernymy between adjectives. We present a theoretical discussion of the hypernymy relation and how it differs for adjectives in contrast to nouns and verbs. We develop a new resource for adjective hypernymy and fine-tune large language models to predict adjective hypernymy, showing that the methodology of TaxoLLaMa can be adapted to this task.
According to Futrell and Mahowald [arXiv:2501.17047], both infants and language models (LMs) find attested languages easier to learn than impossible languages that have unnatural structures. We review the literature and show that LMs often learn attested and many impossible languages equally well. Difficult to learn impossible languages are simply more complex (or random). LMs are missing human inductive biases that support language acquisition.
We argue that human language learning proceeds in a manner that is different in nature from current approaches to training LLMs, predicting a difference in learning biases. We then present evidence from German plural formation by LLMs that confirm our hypothesis that even very powerful implementations produce results that miss aspects of the logic inherent to language that humans have no problem with. We conclude that attention to the different structures of human language and artificial neural networks is likely to be an avenue to improve LLM performance.
John Pavlopoulos, Juli Bakagianni, Kanella Pouli
et al.
Natural Language Processing (NLP) for lesser-resourced languages faces persistent challenges, including limited datasets, inherited biases from high-resource languages, and the need for domain-specific solutions. This study addresses these gaps for Modern Greek through three key contributions. First, we evaluate the performance of open-source (Llama-70b) and closed-source (GPT-4o mini) large language models (LLMs) on seven core NLP tasks with dataset availability, revealing task-specific strengths, weaknesses, and parity in their performance. Second, we expand the scope of Greek NLP by reframing Authorship Attribution as a tool to assess potential data usage by LLMs in pre-training, with high 0-shot accuracy suggesting ethical implications for data provenance. Third, we showcase a legal NLP case study, where a Summarize, Translate, and Embed (STE) methodology outperforms the traditional TF-IDF approach for clustering \emph{long} legal texts. Together, these contributions provide a roadmap to advance NLP in lesser-resourced languages, bridging gaps in model evaluation, task innovation, and real-world impact.
The scarcity of non-English data limits the development of non-English large language models (LLMs). Transforming English-centric LLMs to non-English has been identified as an effective and resource-efficient method. Previous works start from base LLMs and perform knowledge distillation (KD) with data generated by stronger LLMs, e.g. GPT-4. Compared to base LLMs, chat LLMs are further optimized for advanced abilities, e.g. multi-turn conversation and human preference alignment, and thus more powerful in both helpfulness and safety. However, transforming a chat LLM involves two critical issues: (1) How can we effectively transfer advanced abilities without their supervised data? (2) How can we prevent the original knowledge from catastrophic forgetting during transformation? We target these issues by introducing a simple framework called TransLLM. For the first issue, TransLLM divides the transfer problem into some common sub-tasks with the translation chain-of-thought, which uses the translation as the bridge between English and non-English step-by-step. We further enhance the performance of sub-tasks with publicly available data. For the second issue, we propose a method comprising two synergistic components: low-rank adaptation for training to maintain the original LLM parameters, and recovery KD, which utilizes data generated by the chat LLM itself to recover the original knowledge from the frozen parameters. In the experiments, we transform the LLaMA-2-chat-7B to the Thai language. Our method, using only single-turn data, outperforms strong baselines and ChatGPT on multi-turn benchmark MT-bench. Furthermore, our method, without safety data, rejects more harmful queries of safety benchmark AdvBench than both ChatGPT and GPT-4. Code is available at https://github.com/hy5468/TransLLM.
While large language models exhibit certain cross-lingual generalization capabilities, they suffer from performance degradation (PD) on unseen closely-related languages (CRLs) and dialects relative to their high-resource language neighbour (HRLN). However, we currently lack a fundamental understanding of what kinds of linguistic distances contribute to PD, and to what extent. Furthermore, studies of cross-lingual generalization are confounded by unknown quantities of CRL language traces in the training data, and by the frequent lack of availability of evaluation data in lower-resource related languages and dialects. To address these issues, we model phonological, morphological, and lexical distance as Bayesian noise processes to synthesize artificial languages that are controllably distant from the HRLN. We analyse PD as a function of underlying noise parameters, offering insights on model robustness to isolated and composed linguistic phenomena, and the impact of task and HRL characteristics on PD. We calculate parameter posteriors on real CRL-HRLN pair data and show that they follow computed trends of artificial languages, demonstrating the viability of our noisers. Our framework offers a cheap solution for estimating task performance on an unseen CRL given HRLN performance using its posteriors, as well as for diagnosing observed PD on a CRL in terms of its linguistic distances from its HRLN, and opens doors to principled methods of mitigating performance degradation.
Bias studies on multilingual models confirm the presence of gender-related stereotypes in masked models processing languages with high NLP resources. We expand on this line of research by introducing Filipino CrowS-Pairs and Filipino WinoQueer: benchmarks that assess both sexist and anti-queer biases in pretrained language models (PLMs) handling texts in Filipino, a low-resource language from the Philippines. The benchmarks consist of 7,074 new challenge pairs resulting from our cultural adaptation of English bias evaluation datasets, a process that we document in detail to guide similar forthcoming efforts. We apply the Filipino benchmarks on masked and causal multilingual models, including those pretrained on Southeast Asian data, and find that they contain considerable amounts of bias. We also find that for multilingual models, the extent of bias learned for a particular language is influenced by how much pretraining data in that language a model was exposed to. Our benchmarks and insights can serve as a foundation for future work analyzing and mitigating bias in multilingual models.
In recent times, substantial advancements have been witnessed in large language models (LLMs), exemplified by ChatGPT, showcasing remarkable proficiency across a range of complex tasks. However, many mainstream LLMs (e.g. LLaMA) are pretrained on English-dominant corpus, which limits their performance in other non-English languages. In this paper, we focus on how to effectively transfer the capabilities of language generation and following instructions to a non-English language. To answer this question, we conduct an extensive empirical investigation based on LLaMA, accumulating over 1440 GPU hours. We analyze the impact of key factors such as vocabulary extension, further pretraining, and instruction tuning on transfer. To accurately assess the model's level of knowledge, we employ four widely used standardized testing benchmarks: C-Eval, MMLU, AGI-Eval, and GAOKAO-Bench. Furthermore, a comprehensive evaluation of the model's response quality is conducted, considering aspects such as accuracy, fluency, informativeness, logical coherence, and harmlessness, based on LLM-Eval, a benchmarks consisting instruction tasks from 17 diverse categories. Our evaluation results demonstrate that comparable performance to state-of-the-art transfer models can be achieved with less than 1% of the pretraining data, both in terms of knowledge alignment and response quality. Furthermore, the experimental outcomes across the thirteen low-resource languages also exhibit similar trends. We anticipate that the conclusions revealed by the experiments will aid the community in developing non-English LLMs.
Humans are efficient language learners and inherently social creatures. Our language development is largely shaped by our social interactions, for example, the demonstration and feedback from caregivers. Contrary to human language learning, recent advancements in large language models have primarily adopted a non-interactive training paradigm, and refined pre-trained models through feedback afterward. In this work, we explore how corrective feedback from interactions influences neural language acquisition from scratch through systematically controlled experiments, assessing whether it contributes to word learning efficiency in language models. We introduce a trial-and-demonstration (TnD) learning framework that incorporates three distinct components: student trials, teacher demonstrations, and a reward conditioned on language competence at various developmental stages. Our experiments reveal that the TnD approach accelerates word acquisition for student models of equal and smaller numbers of parameters, and we highlight the significance of both trials and demonstrations. We further show that the teacher's choices of words influence students' word-specific learning efficiency, and a practice-makes-perfect effect is evident by a strong correlation between the frequency of words in trials and their respective learning curves. Our findings suggest that interactive language learning, with teacher demonstrations and active trials, can facilitate efficient word learning in language models.
Timmer, Antje, Neuser, Johanna, Uslar, Verena
et al.
Introduction: According to the Master Plan 2020, science education will play a critical role in future medical curricula. Science modules have already been implemented at many locations. Other medical faculties will follow in the next few years, as legislation is expected to make recommendations of the national competence-based learning objectives curriculum for medicine (NKLM) mandatory. This article aims to present an implementation example from epidemiology and biometry as a contribution to the didactic discussions within the data sciences in medicine. Project description: We report on our experiences with a data analysis project for second-year medical students, which has been compulsory at the Faculty of Medicine and Health Sciences since 2019. The project is intended to train the scientific skills required from the subjects of epidemiology and biometry for student research projects. Emphasis is placed on responsible data handling, transparency, and reproducibility. For example, the writing of a statistical analysis plan is required prior to data access. Improved standardization of materials, optional use of the English language, and digital support will be implemented to help manage the project when student numbers increase. Discussion: The experience from five years is very positive, although a formal evaluation of the learning success is still pending. Current challenges concern staffing, additional time and supervision requirements for those students who do statistical programming with R, and improved integration into the medical curriculum.
Computer applications to medicine. Medical informatics, Infectious and parasitic diseases
BackgroundThe electronic health record (EHR) has been widely implemented internationally as a tool to improve health and healthcare delivery. However, EHR implementation has been comparatively slow amongst hospitals in the Arabian Gulf countries. This gradual uptake may be linked to prevailing opinions amongst medical practitioners. Until now, no systematic review has been conducted to identify the impact of EHRs on doctor-patient relationships and attitudes in the Arabian Gulf countries.ObjectiveTo understand the impact of EHR use on patient-doctor relationships and communication in the Arabian Gulf countries.DesignA systematic review of English language publications was performed using PRISMA chart guidelines between 1990 and 2023.MethodsElectronic database search (Ovid MEDLINE, Global Health, HMIC, EMRIM, and PsycINFO) and reference searching restricted to the six Arabian Gulf countries only. MeSH terms and keywords related to electronic health records, doctor-patient communication, and relationship were used. Newcastle-Ottawa Scale (NOS) quality assessment was performed.Results18 studies fulfilled the criteria to be included in the systematic review. They were published between 1992 and 2023. Overall, a positive impact of EHR uptake was reported within the Gulf countries studied. This included improvement in the quality and performance of physicians, as well as improved accuracy in monitoring patient health. On the other hand, a notable negative impact was a general perception of physician attention shifted away from the patients themselves and towards data entry tasks (e.g., details of the patients and their education at the time of the consultation).ConclusionThe implementation of EHR systems is beneficial for effective care delivery by doctors in Gulf countries despite some patients' perception of decreased attention. The use of EHR assists doctors with recording patient details, including medication and treatment procedures, as well as their outcomes. Based on this study, the authors conclude that widespread EHR implementation is highly recommended, yet specific training should be provided, and the subsequent effect on adoption rates by all users must be evaluated (particularly physicians). The COVID-19 Pandemic showed the great value of EHR in accessing information and consulting patients remotely.
This article argues that the data on English evidential markers indicate that evidentiality is not a grammatical category which is only applicable to some languages, but can be considered a universal semantic function that tends to trigger the grammaticalization of lexical forms. This study investigates five criteria that can help us locate linguistic forms on the lexicon–grammar continuum: reduction, desemanticization, backgrounding, decategorialization and paradigmatization. Although it has not reached the full maturity of so-called ‘evidential languages’, English provides evidence of the grammaticalization of evidentiality. Evidentiality is thus a relevant notion for English which substantially impacts the organization of the lexicon of the language, and the evolution of its grammar.
This critical exploratory study aims to examine the role academic brokers play in opening (or not) the gates to non-first-language-English (NFLE) scholars to contribute to the global research conversation. For the study, a qualitative research approach was used to collect data; ten emergent and established researchers were interviewed, all of whom originated from non-Anglophone countries. Four academic brokers were also interviewed to further examine the topic from their viewpoints. The findings revealed that revisions recommended by journal editors and reviewers could perhaps diminish the richness of texts and ultimately affect the voices NFLE authors try to project in their papers. Findings also showed that academic brokers are cognizant of the problems NFLE authors face when writing for publication, especially those pertaining to the quality of their writing and to the ways they respond to reviewers’ suggestions and handle the review process.