Simple Text Research Articles

Advancing Literary Interpretation via Critical Reading Proficiency: A Sustainable EFL Framework for Generation Z

This research investigates the transformative potential of literary analysis in advancing English proficiency among Egyptian EFL learners, particularly those in the Faculty of Arts and Humanities. The study focuses on the linguistic and analytical challenges encountered by recent public-school graduates in the Suez Governorate, where English instruction is limited to simplified texts and lacks critical engagement. Unlike their counterparts from language schools in Cairo, these students enter university without placement tests, relying solely on their high school grades, which results in underdeveloped critical reading and thinking skills. Sixty Egyptian students participated in this experimental quantitative research design. Drawing on the theoretical framework of Stylistics—a linguistic and literary approach—the paper underscores the value of applying systematic textual analysis to literary works. Stylistics, as a tool for bridging linguistic form and literary meaning, enables students to enhance both their linguistic and interpretive capabilities. By engaging with literary texts through this dual approach, learners develop deeper cognitive skills and greater critical engagement with language. The research argues for integrating Stylistics into the EFL curriculum as a sustainable pedagogical framework to cultivate critical reading proficiency, thereby supporting students’ overall linguistic development and equipping them for professional careers in the arts and humanities. This integration fosters a more profound understanding of language and literature, promoting sustainable language education for Generation Z.

Automatic Text Simplification for Lithuanian: Transforming Administrative Texts into Plain Language

Automatic Text Simplification for Lithuanian: Transforming Administrative Texts into Plain Language

The impact of generative AI on school music education: Challenges and recommendations

The widespread application of generative artificial intelligence (AI) poses both opportunities and challenges in formal music education. Students can now effortlessly compose using simple text prompts with AI music generators, a situation which encourages innovative pedagogical approaches and democratizes creativity in the music classroom. However, concerns about cultural bias, originality, equity, and the ethical use of generative AI in school music education require careful regulation. This article highlights both the potential benefits and risks through a review of current applications of generative AI in music education, in the process proposing a set of policy recommendations that can serve to ethically and effectively guide its use. These include enhancing AI literacy among students and teachers, developing assessment frameworks that reflect the collaborative nature of AI-assisted music creation, defining acceptable boundaries in terms of ensuring equitable access to AI tools, and providing professional development opportunities for music teachers. By maintaining an awareness of technological advancements and their impact on students’ musical engagement, school music education can remain relevant to young people’s evolving musical experiences while simultaneously preparing them to engage confidently, creatively, and critically with the complex landscape of generative AI.

The impact of text format on the quality of students' reading performance

Introduction. The rapid proliferation of digital technologies and the widespread adoption of electronic texts in the educational process have raised the question of the impact of text presentation mode on the quality of students' reading. This issue is especially important during the period of development of reading skills in primary school. This article describes the results of an experimental study conducted to investigate the influence of text presentation mode on the reading performance of primary and secondary school students. Materials and methods. To achieve the research goals and objectives, an experimental design with repeated measures and control of additional variables using counterbalancing was developed. Participants were asked to read simplified and complicated texts in electronic and print formats sequentially. The experiment was conducted in two stages to assess the reading quality of simplified narrative texts and texts with a complex rhetorical structure. The experiment involved 107 primary school students aged 7 to 10 years (mean age = 9.33, 53.33% girls) and 30 secondary school students aged 11 to 15 years (mean age = 12.33, 53.33% girls). Results. The study found no significant differences in younger students' reading speed, memorization performance, and comprehension of simple narrative texts presented in electronic and print formats (163≤T≤648; 0,24≤p≤0,97). As students matured, they showed higher memorization performance for simple electronic texts compared to printed texts (χ 2 =5,98; p=0,047 – text «MA»; χ 2 =8,79; p=0,012 – text «AA»). However, the text format did not affect reading speed. The significance of the text presentation mode was amplified when perceiving complex texts. Girls showed a slower reading speed for complex electronic texts (T=89,5; p=0,0097), boys – lower remembering (T=12; p=0,019) and comprehension (T=56; p=0,039). All the groups showed lower memorization quality of electronic texts compared to printed texts (T=292; p=0,024). Conclusion. The study results demonstrated that the text presentation format (electronic/print) influences the reading performance of primary and secondary school students. However, this influence is not uniform and depends on several factors, such as the content of the text, its complexity, age, gender, and individual psychological characteristics of the readers. The results highlight the importance of a selective approach to the use of electronic texts in school education.

A Rule-Based Parser in Comparison with Statistical Neuronal Approaches in Terms of Grammar Competence

The “Easy Language” standard was created to help individuals with cognitive disabilities understand texts more easily. Typically, text simplification is performed by language experts and is available for limited materials. We introduce a new software tool designed to analyze and simplify any text according to the “Easy Language” rules. This tool uses a rule-based system, conducting a full grammatical analysis of each sentence and then simplifying it into a grammatically correct form. Unlike neuronal approaches, which are based on statistics and are very popular today, our rule-based approach explicitly addresses language ambiguities by examining all possible interpretations and eliminating the incorrect ones. The purpose of the present study is to compare the performance of our rule-base parser with two state-of-the-art statistical parsers, one based on dependencies between words (SpaCy parser) and the other based on linguistic constituents (Stanford parser). Although large language models (LLMs), which are the technical basis of the software ChatGPT, were not designed specifically for grammatical parsing, because of their popularity, users, especially language learners, often ask them grammatical questions as well. Therefore, we use LLMs as supplementary models for comparison. LMMs produce grammatically correct text on any topic; however, their grammar knowledge is implicit within the trained weights. To evaluate how well state-of-the-art methods can perform a grammatical analysis, we parse ten sentences with our tool, the statistical parsers from SpaCy and Stanford, and ask two LLMs equivalent grammar questions. The results show that our rule-based method provides a more informative and reliable grammatical analysis compared to these two parsers and outperforms LLMs in that specific task.

The Adaptation Of Sabahattin Ali’s Stories “The Unrescuable Masterpiece” And “The Swallows” For B2 Language Proficiency Level

Language, which is a qualified tool for the socialization of people, is a phenomenon that has continued its existence from past to present. Societies developing with language have come a long way in transferring their linguistic productions over time. In this context, written texts created for cultural transmission and literary pleasure in Turkish are one of the elements that best describe human communication with language. In this context, literary texts created over time with the accumulation of societies have an important place in both mother tongue teaching and mother tongue teaching in the learning and teaching process. In the process of teaching Turkish as a foreign language to individuals who have come together from different cultures in the process of teaching Turkish as a foreign language, the use of literary texts emerging from the accumulation of society in the process will make learning permanent. The research aims to contribute to the field of teaching Turkish as a foreign language within the scope of simplified texts. In the process, the main idea was preserved by preserving the meaning structure given by the text. The related stories were taken from the 33rd edition of Sabahattin Ali's Yapı Kredi Publications published in 2018. In this respect, the main purpose is to enable the student to understand the text better. Bulding on this perspective, the research is based on the adaptation of Sabahattin Ali's stories titled The Unrescuable Masterpiece and The Swallows, which are included in the story book called The Mill, taking into account the B2 level criteria of the Common European Framework of Reference. The related stories were adapted and restructured through text simplification. In the process, the simplification process was reorganized by paying attention to the textuality criteria and using the appropriate methods of text preservation, text modification, text summarization and deletion. This study, which includes document analysis and descriptive analysis methods from qualitative research methods, the stories discussed in this study are aimed to arouse an aesthetic pleasure in the reader and improve reading comprehension skills in the teaching process, and within the framework of the adapted text, various examples of practical activities to be used in the course are included.

Shadclips: When Parameter-Efficient Fine-Tuning with Multimodal Meets Shadow Removal

Segment Anything Model (SAM), an advanced universal image segmentation model trained on an expansive visual dataset, has set a new benchmark in image segmentation and computer vision. However, it faced challenges when it came to distinguishing between shadows and their backgrounds. To address this, we proposed ShadClips, which consists of SAM-optimizer and SONet. It has dramatically enhanced SAM’s ability to segment shadow images, differentiating between the background and both soft and hard shadows adeptly. Due to its dependence on pixel point inputs, the SAM-Optimizer interface could do better. This method presents challenges, especially when dealing with long, extended shadows. To make the user experience more intuitive and effective, we incorporated the capabilities of CLIPs. Therefore, simple text descriptions like “A photo of a shadow” can be used to guide the SAM-Optimizer, allowing it to select the most relevant shadow mask from SAM’s comprehensive category list. Meanwhile, we introduce SONet to shadow removal. A large number of experiments on ISTD/SRD prove that the proposed method is effective and satisfactory. The source code of the ShadClips can be accessed from https://github.com/zhangbaijin/SAM-helps-Shadow .

تحديات استخدام أدوات الذكاء الاصطناعي التوليدي في تطوير بيئات التعلم القائمة على الوسائط المتعددة من وجهة نظر الخبراء

The current study aimed to identify the challenges of generative artificial intelligence in developing multimedia-based learning environments, as researchers saw that generative artificial intelligence tools for multimedia have spread rapidly in the last three years, through which simple text processors can generate digital objects in the form of video, images and animations. Since educational environments are almost devoid of multimedia, it is important to examine the challenges that generative intelligence tools can present to multimedia-based educational environments. Since the use of multimedia is primarily based on the principles of the cognitive theory of multimedia learning, reviewing the challenges associated with the use of generative intelligence tools will be linked to the principles of the cognitive theory of multimedia learning. The current research relied on the phenomenological approach in conducting a set of interviews with (6) experts specialized in the use of generative intelligence tools in the field of multimedia. According to the objective analysis processes, the most prominent results were that the challenges are based on educational challenges, organizational, legislative, technical and technological challenges.

Automatic text simplification for French: model fine-tuning for simplicity assessment and simpler text generation

Automatic text simplification models face the challenge of generating outputs that, while being indeed simpler, still retain some complexity. This stems from the inherently relative nature of simplification, wherein a given text is transformed into a relatively simpler version, which does not necessarily equate to simple. We thus aim to propose a finer-grained method to assess sentence complexity in French. Our solution comprises three models, in which two address absolute and relative sentence complexity assessment, while the third focuses on measuring simplicity gain. By employing this triad of models, we aim to offer a comprehensive approach to qualify and quantify sentence simplicity. Our approach utilizes FlauBERT, fine-tuned for classification and regression tasks. Based on our three-dimensional complexity analysis, we provide the WiViCo dataset, comprising 46,525 aligned complex-simpler pairs, which is further leveraged to fine-tune different FLAN-T5-based language models for simplified text generation. In this context, we perform different evaluation tasks that contrast human evaluations with BLEU and SARI metrics for the generated simplifications, the models’ computational efficiency and environmental impact.

Automatic Simplification of Lithuanian Administrative Texts

Text simplification reduces the complexity of text while preserving essential information, thus making it more accessible to a broad range of readers, including individuals with cognitive disorders, non-native speakers, children, and the general public. In this paper, we present experiments on text simplification for the Lithuanian language, aiming to simplify administrative texts to a Plain Language level. We fine-tuned mT5 and mBART models for this task and evaluated the effectiveness of ChatGPT as well. We assessed simplification results via both quantitative metrics and qualitative evaluation. Our findings indicated that mBART performed the best as it achieved the best scores across all evaluation metrics. The qualitative analysis further supported these findings. ChatGPT experiments showed that it responded quite well to a short and simple prompt to simplify the given text; however, it ignored most of the rules given in a more elaborate prompt. Finally, our analysis revealed that BERTScore and ROUGE aligned moderately well with human evaluations, while BLEU and readability scores indicated lower or even negative correlations

Abstract 4116844: Simplifying Cardiology Research Abstracts: Assessing ChatGPT's Readability and Comprehensibility for Non-Medical Audiences

Background: Artificial Intelligence (AI)-powered chatbots like ChatGPT are increasingly used in academic medical settings to help with tasks such as evidence synthesis and manuscript drafting. They have shown potential in simplifying complex medical texts for non-medical audiences like patients and journalists. However, less is known about whether simplified texts may exclude important information or be of interest to patients or other non-medically-trained people such as journalists. Objective: This study aims to assess ChatGPT's capacity to simplify cardiology research abstracts by eliminating jargon and enhancing universal comprehension. Methods: We analyzed all abstracts and scientific statements published from July to November 2023 in Circulation (n=113). These abstracts were processed through ChatGPT with the prompt: "Please rewrite the following text to be comprehensible at a 5th-grade reading level. Retain all original information and exclude nothing". We assessed the readability of both original and simplified texts using Flesch-Kincaid Grade Level (FKGL) and Reading Ease (FKRE) scores. Additionally, a panel of five physicians and five laypeople evaluated these texts for completeness, accuracy, and understandability. Results: ChatGPT transformation or abstracts reduced the required reading level from a college graduate to 8-9th grade by both FKGL (18.3 to 8.6; p<.001) and FKRE (14.6 to 68.5; p<.001). The physician panel generally found that the simplified abstracts retained all essential information without introducing errors. Both physicians and laypeople found the simplified texts more comprehensible. Laypeople appreciated the ease of understanding but did not strongly express a desire for more such simplified texts, and some still worried about the simplified abstracts missing important details. Conclusion: ChatGPT simplifies medical research abstracts to a level understandable by those with less than a high school education without excluding important information. This approach may be useful to communicate new research findings to patients and other non-medically-trained people.

Eye movements in oral reading by primary school students: The relevance of punctuation marks

This study aims to explore the relevance of punctuation marks during oral reading. To achieve this objective, we compared eye movement metrics of two groups of 5th-year students, classified based on the Reading Fluency variable. The fluent reading group (FL) consisted of 27 participants, while 31 participants were in the poor reading fluent group (PF). All participants read three types of text: a simple text with punctuation, a complex text with punctuation and a simple text without punctuation. By analyzing the fixations and saccades, the data suggest that the complexity of the text influences the eye-tracking metrics of the PF group, but not the FL group. On the other hand, the presence of punctuation influence the reading behavior of the FL group, but not the PF group. These findings highlight the influence of punctuation and textual complexity on the dynamics of oral reading and offer valuable insights into how reading fluency is related to text comprehention.

Exploring and enhancing the accessibility of children's oral health resources (called HABIT) for high risk communities.

Within the city of Bradford in West Yorkshire, South Asian and Eastern European communities have an increased risk of childhood tooth decay, especially among families with Limited English Proficiency. Tooth decay is preventable, with national guidelines advocating home-based behaviours (toothbrushing with fluoride toothpaste and reducing sugar intake). In England, Health Visitors have opportunities to undertake oral health conversations during universal visits for children aged 0-24 months. The HABIT (Health visitors delivering Advice in Britain on Infant Toothbrushing) intervention provides structured oral health conversations, underpinned by complex intervention methodology. A feasibility study found HABIT acceptable to parents, feasible to deliver and led to improvements in home-based behaviours. However, the reach of this original study was limited to those proficient in English. This new study focused on exploring and enhancing the accessibility of the HABIT intervention to parents with Limited English Proficiency. Twenty-four parents participated in interviews and focus groups, with 21 requesting support from interpreters. Community centres and WhatsApp were used to maximise inclusivity. Interviews and focus groups, followed a topic guide and the "Think Aloud" technique, were professionally transcribed, managed in NVivo, and thematically analysed. Team discussions facilitated analytical rigour. Recruitment continued until data saturation. Three themes were developed: (1) Navigating linguistic barriers; (2) Engagement through visuals; and (3) Addressing oral health challenges. Parents employed diverse strategies to interpret resources, including Google Translate, as well as family and wider community members. Consequently, the HABIT resources were modified to include simple text, subtitles, and translation tools. Parents highlighted the benefits of shorter oral health messages with clear visuals to help understanding, and this strategy was applied across all resources. Challenges surrounding children's resistance to toothbrushing, high sugar intake within their wider families and communities, and limited dental access were all raised. The HABIT resources were updated to address these challenges. Collaborative community engagement has enhanced the HABIT resources, enabling access for high-risk communities to preventive oral-health programmes thereby promoting health equity.

Efficient indexing and querying of annotations in a pangenome graph.

The current reference genome is the backbone of diverse and rich annotations. Simple text formats, like VCF or BED, have been widely adopted and helped the critical exchange of genomic information. There is a dire need for tools and formats enabling pangenomic annotation to facilitate such enrichment of pangenomic references. The Graph Alignment Format (GAF) is a text format, tab-delimited like BED/VCF files, which was proposed to represent alignments. GAF could also be used to store paths representing annotations in a pangenome graph, but there are no tools to index and query them efficiently. Here, we present extensions to vg and HTSlib that provide efficient sorting, indexing, and querying for GAF files. With this approach, annotations overlapping a subgraph can be extracted quickly. Paths are sorted based on the IDs of traversed nodes, compressed with BGZIP, and indexed with HTSlib/tabix via our extensions for the GAF format. Compared to the binary GAM format, GAF files are easier to edit or inspect because they are plain text, and we show that they are twice as fast to sort and half as large on disk. In addition, we updated vg annotate, which takes BED or GFF3 annotation files relative to linear sequences and projects them into the pangenome. It can now produce GAF files representing these annotations' paths through the pangenome. We showcase these new tools on several applications. We projected annotations for all Human Pangenome Reference Consortium Year 1 haplotypes, including genes, segmental duplications, tandem repeats and repeats annotations, into the Minigraph-Cactus pangenome (GRCh38-based v1.1). We also projected known variants from the GWAS Catalog and expression QTLs from the GTEx project into the pangenome. Finally, we reanalyzed ATAC-seq data from ENCODE to demonstrate what a coverage track could look like in a pangenome graph. These rich annotations can be quickly queried with vg and visualized using existing tools like the Sequence Tube Map or Bandage.

Exploring Large Language Models to generate Easy to Read content

Ensuring text accessibility and understandability are essential goals, particularly for individuals with cognitive impairments and intellectual disabilities, who encounter problems accessing information across various mediums such as web pages, newspapers, online administrative tasks, or health documents. Initiatives like Easy to Read and Plain Language guidelines aim to simplify complex texts; however, standardizing these guidelines remains challenging and often involves manual processes. This work presents an exploratory investigation into leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) approaches to simplify Spanish texts into Easy to Read formats, with a focus on utilizing Large Language Models (LLMs) for creating accessible texts, especially in generating Easy to Read content. The study contributes a parallel corpus of Spanish adapted for Easy To Read format, which serves as a valuable resource for training and testing text simplification systems. Additionally, several text simplification experiments using LLMs and the collected corpus are conducted, involving fine-tuning and testing a Llama2 model to generate Easy to Read content. A qualitative evaluation, guided by an expert in text adaptation for Easy to Read content, is carried out to assess the automatically simplified texts. This research contributes to advancing text accessibility for individuals with cognitive impairments, highlighting promising strategies for leveraging LLMs while responsibly managing energy usage.

Integrating Literacy Programs to Strengthen School Safety Climate: A Research-Based Approach

Objective: This study aims to support the implementation of literacy programs in schools to enhance the school safety climate. The program targets the negative impacts of bullying and violence and helps students develop conflict resolution skills to reduce harmful incidents. Theoretical Framework: Academic literacy is defined as the ability to read and comprehend texts within specific content areas in a school setting. This includes understanding simple texts as well as addressing difficulties with more complex materials often encountered in school. Method: A quantitative approach with path analysis and purposive sampling was used, with the Taro Yamane method applied to select relevant samples. The study examines five dimensions of the school safety climate: Teacher-Student Relationship Quality, Student Character and Behavior, Peer Relationships, School Safety Climate Quality, and Bullying Frequency. Results and Discussion: The findings reveal that Teacher-Student Relationships, Student Behavior, and Peer Relationships all have a direct impact on the quality of the school safety climate. The school safety climate, in turn, significantly influences the overall school environment. Research Implications: The study suggests that integrating literacy programs can significantly improve the school safety climate by enhancing relationships and reducing bullying. Originality/Value: The integration of literacy programs represents a novel approach to fostering a safer and more supportive school environment. These findings offer practical insights for schools aiming to enhance their safety climates and support student development.

Nursing Records Regarding Decision-Making in Cancer Supportive Care: A Retrospective Study in Japan.

This study was performed to examine the content of decision-making support and patient responses, as documented in the nursing records of individuals with cancer. These patients had received outpatient treatment at hospitals that met government requirements for providing specialized cancer care. Nursing records from the electronic medical record system (in the subjective, objective, assessment, and plan [SOAP] format), along with data from interviews, were extracted for patients receiving outpatient care at the Department of Internal Medicine and Palliative Care and the Department of Breast Oncology. Data analysis involved simple tabulation and text mining, utilizing KH Coder version 3.beta.07d. The study included 42 patients from palliative care internal medicine and 60 from breast oncology, with mean ages of 70.5 ± 12.2 and 55.8 ± 12.2 years, respectively. Decisions most frequently regarded palliative care unit admission (25 cases) and genetic testing (24 cases). The assessment category covered keywords including (1) "pain," "treatment," "future," "recuperation," and "home," as terms related to palliative care and internal medicine, as well as (2) "treatment," "relief," and "genetics" as terms related to breast oncology. The plan category incorporated keywords such as (1) "treatment," "relaxation," and "visit" and (2) "explanation," "confirmation," and "conveyance." Nurses appear crucial in evaluating patients' symptoms and treatment paths during the decision-making support process, helping them make informed choices about future treatments, care settings, and genetic testing. However, when patients cannot make a decision solely based on the information provided, clinicians must address complex psychological concepts such as disease progression and the potential genetic impact on their children. Further detailed observational studies of nurses' responses to patients' psychological reactions are warranted.

Accuracy of a Commercial Large Language Model (ChatGPT) to Perform Disaster Triage of Simulated Patients Using the Simple Triage and Rapid Treatment (START) Protocol: Gage Repeatability and Reproducibility Study.

The release of ChatGPT (OpenAI) in November 2022 drastically reduced the barrier to using artificial intelligence by allowing a simple web-based text interface to a large language model (LLM). One use case where ChatGPT could be useful is in triaging patients at the site of a disaster using the Simple Triage and Rapid Treatment (START) protocol. However, LLMs experience several common errors including hallucinations (also called confabulations) and prompt dependency. This study addresses the research problem: "Can ChatGPT adequately triage simulated disaster patients using the START protocol?" by measuring three outcomes: repeatability, reproducibility, and accuracy. Nine prompts were developed by 5 disaster medicine physicians. A Python script queried ChatGPT Version 4 for each prompt combined with 391 validated simulated patient vignettes. Ten repetitions of each combination were performed for a total of 35,190 simulated triages. A reference standard START triage code for each simulated case was assigned by 2 disaster medicine specialists (JMF and MV), with a third specialist (LC) added if the first two did not agree. Results were evaluated using a gage repeatability and reproducibility study (gage R and R). Repeatability was defined as variation due to repeated use of the same prompt. Reproducibility was defined as variation due to the use of different prompts on the same patient vignette. Accuracy was defined as agreement with the reference standard. Although 35,102 (99.7%) queries returned a valid START score, there was considerable variability. Repeatability (use of the same prompt repeatedly) was 14% of the overall variation. Reproducibility (use of different prompts) was 4.1% of the overall variation. The accuracy of ChatGPT for START was 63.9% with a 32.9% overtriage rate and a 3.1% undertriage rate. Accuracy varied by prompt with a maximum of 71.8% and a minimum of 46.7%. This study indicates that ChatGPT version 4 is insufficient to triage simulated disaster patients via the START protocol. It demonstrated suboptimal repeatability and reproducibility. The overall accuracy of triage was only 63.9%. Health care professionals are advised to exercise caution while using commercial LLMs for vital medical determinations, given that these tools may commonly produce inaccurate data, colloquially referred to as hallucinations or confabulations. Artificial intelligence-guided tools should undergo rigorous statistical evaluation-using methods such as gage R and R-before implementation into clinical settings.

Transformer-Based Regression Models for Assessing Reading Passage Complexity: A Deep Learning Approach in Natural Language Processing

Natural Language Processing (NLP) is a vital area in deep learning, widely applied in tasks like text classification, virtual assistants, speech recognition, and autocorrect features in digital devices. It allows machines to understand and generate human language, enhancing user interactions with software. This paper presents a deep learning model using the Transformer architecture for a regression task to predict the complexity of reading passages based on text excerpts. By leveraging the Transformer’s capability to identify complex patterns in text, the model achieves a relative error rate of about 10%. The paper also examines how different architectural choices influence model performance, focusing on one-hot encoding and embeddings. While one-hot encoding provides a simple text representation, embeddings offer a richer, more nuanced understanding of word relationships. The findings highlight the significance of model design and data representation in optimizing NLP tasks, providing insights for future advancements in the field.

ZNO-Eval: Benchmarking reasoning capabilities of large language models in Ukrainian

As the usage of large language models for problems outside of simple text understanding or generation increases, assessing their abilities and limitations becomes crucial. While significant progress has been made in this area over the last few years, most research has focused on benchmarking English, leaving other languages underexplored. This makes evaluating the reasoning and robustness level of language models in Ukrainian particularly challenging. The purpose of this work is to establish a comprehensive benchmark for the reasoning capabilities evaluation of large language models in the Ukrainian language. This paper presents the ZNO-Eval benchmark based on real exam tasks from Ukraine's standardized educational testing system: the External Independent Evaluation and the National Multi-subject Test. With single- answer options, multiple-choice, matching, and open-ended questions from diverse subjects, including Ukrainian language, mathematics, history, and geography, this dataset paves the way toward a thorough analysis of reasoning capabilities across different domains and complexities. Evaluation of several well-known language models, such as GPT-3.5-Turbo, GPT-4o, GPT-4-Turbo, Mistral Large, Claude 3 Opus, and Gemini-1.5 Pro on this benchmark demonstrated the superiority of GPT-4o in both common knowledge reasoning and intricate language tasks. At the same time, Gemini Pro and GPT-4 Turbo excelled in the arithmetic domain, leading in single-answer and open-ended math problems. While all models were close to max performance in text-only common knowledge tasks like history and geography, there still is a gap for Ukrainian language and math, thus highlighting the importance of developing specialized language benchmarks for more accurate assessments of model capabilities and limitations across different languages and contexts. This research introduced ZNO-Eval, an effective benchmark for evaluating reasoning capabilities, and thoroughly explored the abilities and limitations of modern solutions in the Ukrainian language. Future research should aim to expand the scope of ZNO-Eval to other modalities like images commonly used for exam problem description.