Hasil untuk "Japanese language and literature"

Lokman I. Meho, Kiduk Yang

Kazumi Hirano, Keiko Aizawa

<b>Background/Objectives</b>: Evidence on postmortem grief care for family caregivers after home-based end-of-life care is limited. This scoping review aimed to map the content and effects of such interventions for adult family caregivers after home deaths. <b>Methods</b>: Following the Joanna Briggs Institute and Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines, we searched PubMed, Cumulative Index to Nursing and Allied Health Literature, Embase, Cochrane Library, and Ichushi-Web from database inception to 31 March 2024. We included English- or Japanese-language intervention studies performed in home and community settings. “Early” grief care was defined as (i) support initiated within 6 months after the death of a loved one and (ii) interventions initiated during caregiving that assessed bereavement outcomes within 6 months after the death of a loved one. Data were charted and descriptively summarized. <b>Results</b>: From 4766 records, six studies were selected for the review (five randomized controlled trials and one ongoing registry trial). Interventions varied from dyadic psychological sessions integrated into specialist palliative home care (DOMUS) to brief psychoeducation, structured family-physician consultations, general-practice bereavement management with screening and stepped care, remote monitoring with nurse coaching during home hospice care, with bereavement outcomes assessed at 6 months (SCH), and an online self-help program for widowed older adults. The effects were mixed. DOMUS showed a small but significant reduction in caregiver anxiety; SCH reduced caregiver burden during caregiving and improved bereavement adjustment at 6 months. Other interventions did not demonstrate a clear advantage in outcomes over usual care. <b>Conclusions</b>: Early grief care after home-based end-of-life care is heterogeneous. Need-responsive multicomponent models embedded in existing home and community care pathways warrant further theory-informed evaluation.

Xiaohui Zou, Lijun Ke, Shunpeng Zou

The purpose of this study is to introduce a new model of teaching Chinese as a foreign language from the perspective of integrating wisdom. Its characteristics are as follows: focusing on the butterfly model of interpretation before translation, highlighting the new method of bilingual thinking training, on the one hand, applying the new theory of Chinese characters, the theory of the relationship between language and speech, and the forward-looking research results of language science; On the other hand, the application of the new model of teaching Chinese as a foreign language, AI empowering teaching and learning, and the forward-looking research results of educational science fully reflect a series of characteristics of the new model of teaching Chinese as a foreign language from the perspective of integrating wisdom. Its beneficial effects are: not only the old view of language and education, especially the old view of teaching Chinese as a foreign language, but also the old view of human-computer interaction. Its significance lies in that a series of great cross-border Rongzhixue such as language, knowledge, education and teaching, as well as new methods and new topics of bilingual thinking training are clearly put forward from the perspective of integrating wisdom. Especially in the face of the challenge of Chat GPT to human learning ability and even creativity, the existing concepts of language knowledge education and teaching are already very backward. The old concepts of Chinese language education, and teaching Chinese as a foreign language are all facing a series of subversive innovation challenges. How to seek changes in adaptation? This study has made a series of innovative attempts, hoping to benefit academic colleagues, teachers and students.

Stefano M. Iacus, Haodong Qi, Jiyoung Han

This paper presents a hybrid framework for literature reviews that augments traditional bibliometric methods with large language models (LLMs). By fine-tuning open-source LLMs, our approach enables scalable extraction of qualitative insights from large volumes of research content, enhancing both the breadth and depth of knowledge synthesis. To improve annotation efficiency and consistency, we introduce an error-focused validation process in which LLMs generate initial labels and human reviewers correct misclassifications. Applying this framework to over 20000 scientific articles about human migration, we demonstrate that a domain-adapted LLM can serve as a "specialist" model - capable of accurately selecting relevant studies, detecting emerging trends, and identifying critical research gaps. Notably, the LLM-assisted review reveals a growing scholarly interest in climate-induced migration. However, existing literature disproportionately centers on a narrow set of environmental hazards (e.g., floods, droughts, sea-level rise, and land degradation), while overlooking others that more directly affect human health and well-being, such as air and water pollution or infectious diseases. This imbalance highlights the need for more comprehensive research that goes beyond physical environmental changes to examine their ecological and societal consequences, particularly in shaping migration as an adaptive response. Overall, our proposed framework demonstrates the potential of fine-tuned LLMs to conduct more efficient, consistent, and insightful literature reviews across disciplines, ultimately accelerating knowledge synthesis and scientific discovery.

Jongyoon Song, Sangwon Yu, Sungroh Yoon

Negative bias refers to the tendency of large language models (LLMs) to excessively generate negative responses in binary decision tasks (e.g., yes-no question answering). Previous research has focused on detecting and addressing negative attention heads that induce negative bias. However, the underlying detailed factors influencing negative bias remain underexplored. In this paper, we demonstrate that LLMs exhibit format-level negative bias, meaning the prompt format more influences their responses than the semantics of the negative response. For the fine-grained study of the negative bias, we introduce a pipeline for constructing the evaluation set, which systematically categorizes the dataset into three subsets based on the model's parametric knowledge: correct, incorrect, and insufficient relevant knowledge. Through analysis of this evaluation set, we identify a shortcut behavior in which models tend to generate negative responses when they lack sufficient knowledge to answer a yes-no question, leading to negative bias. We further examine how negative bias changes under various prompting scenarios related to parametric knowledge. We observe that providing relevant context and offering an "I don't know" option generally reduces negative bias, whereas chain-of-thought prompting tends to amplify the bias. Finally, we demonstrate that the degree of negative bias can vary depending on the type of prompt, which influences the direction of the response. Our work reveals the various factors that influence negative bias, providing critical insights for mitigating it in LLMs.

Nevidu Jayatilleke, Nisansa de Silva

Solving the problem of Optical Character Recognition (OCR) on printed text for Latin and its derivative scripts can now be considered settled due to the volumes of research done on English and other High-Resourced Languages (HRL). However, for Low-Resourced Languages (LRL) that use unique scripts, it remains an open problem. This study presents a comparative analysis of the zero-shot performance of six distinct OCR engines on two LRLs: Sinhala and Tamil. The selected engines include both commercial and open-source systems, aiming to evaluate the strengths of each category. The Cloud Vision API, Surya, Document AI, and Tesseract were evaluated for both Sinhala and Tamil, while Subasa OCR and EasyOCR were examined for only one language due to their limitations. The performance of these systems was rigorously analysed using five measurement techniques to assess accuracy at both the character and word levels. According to the findings, Surya delivered the best performance for Sinhala across all metrics, with a WER of 2.61%. Conversely, Document AI excelled across all metrics for Tamil, highlighted by a very low CER of 0.78%. In addition to the above analysis, we also introduce a novel synthetic Tamil OCR benchmarking dataset.

Linxuan Wang, Shuiyuan Yu

To explore the relationship between dependency distance (DD) and hierarchical distance (HD) in Japanese, we compared the probability distributions of DD and HD with and without sentence length fixed, and analyzed the changes in mean dependency distance (MDD) and mean hierarchical distance (MHD) as sentence length increases, along with their correlation coefficient based on the Balanced Corpus of Contemporary Written Japanese. It was found that the valency of the predicates is the underlying factor behind the trade-off relation between MDD and MHD in Japanese. Native speakers of Japanese regulate the linear complexity and hierarchical complexity through the valency of the predicates, and the relative sizes of MDD and MHD depend on whether the threshold of valency has been reached. Apart from the cognitive load, the valency of the predicates also affects the probability distributions of DD and HD. The effect of the valency of the predicates on the distribution of HD is greater than on that of DD, which leads to differences in their probability distributions and causes the mean of MDD to be lower than that of MHD.

Teguh Santoso, Slamet, Miftah Nugroho

Criticism plays a crucial role in improving individual and team performance and addressing shortcomings in workplace processes. However, the way criticism is delivered significantly impacts how it is received. This study examines politeness strategies in delivering criticism in Japanese workplaces, drawing on Nguyen's (2005) politeness theory. Using the Discourse Completion Test (DCT) method, the study involved 25 students from the Japanese Literature Program at Universitas Ngudi Waluyo who work in Japan. The findings reveal that direct criticism is rarely used in Japanese workplaces as it risks disrupting wa (harmony) and causing embarrassment, particularly in formal or peer relationships. Direct criticism is employed only in urgent situations or within hierarchical relationships, characterized by explicit, firm language aimed at immediate correction. Conversely, indirect criticism is more prevalent, utilizing linguistic strategies such as suggestions, invitations, or questions to maintain politeness and relational stability. From a sociopragmatic perspective, cultural norms, status hierarchies, and social contexts influence the choice of criticism strategies. From a pragmalinguistic perspective, subtle and implicit linguistic elements are effective in preserving harmony and avoiding confrontation. The study concludes that the success of delivering criticism relies on balancing communicative effectiveness with maintaining harmonious workplace relationships.

A. D. Bertova

The blind in Japan have created their specific culture. As early as in the 14th century, they organized their own guild (Tōdōza) and succeeded in monopolizing a number of traditional entertainment and medical practices, having acquired a rather stable financial position. However, after the end of the Tokugawa shogunate and during the first reforms of the Meiji period, the guild was abolished together with its monopoly, and the blind found themselves in difficult circumstances, having to compete with the sighted, without practical support from the new government. In the early Meiji period, these were mostly Christian missions and private philanthropists who undertook measures to promote education of the blind and fought for their rights and welfare. Christian organizations founded first schools for the blind, such as the Tokyo School for the Blind and Dumb, which made many blind people wishing to acquire education interested in Christianity. The blind often became Christian converts and plunged into educational and philanthropical activities themselves. Major achievements in modernizing the life of the blind in Japan were made due to the work of blind Christians. Blind Christians launched the first Japanese newspaper and one of the first magazines for the blind, were the first among persons with visual impairments in Japan to get higher education, founded the first braille library and one of the first charity funds for the blind. Christianity not only contributed to the rise in living and educational standards of the blind, but also gave them possibilities to discover new ways of self-realization in acquiring new professions as well as in the sphere of spiritual development. For its followers, Christianity eradicated the concept of karma-bound blindness spread in traditional Japan and empowered them with the idea of their special mission in society entrusted to them by God.

Hyungjun Yoon, Biniyam Aschalew Tolera, Taesik Gong et al.

Large language models (LLMs) have demonstrated exceptional abilities across various domains. However, utilizing LLMs for ubiquitous sensing applications remains challenging as existing text-prompt methods show significant performance degradation when handling long sensor data sequences. We propose a visual prompting approach for sensor data using multimodal LLMs (MLLMs). We design a visual prompt that directs MLLMs to utilize visualized sensor data alongside the target sensory task descriptions. Additionally, we introduce a visualization generator that automates the creation of optimal visualizations tailored to a given sensory task, eliminating the need for prior task-specific knowledge. We evaluated our approach on nine sensory tasks involving four sensing modalities, achieving an average of 10% higher accuracy than text-based prompts and reducing token costs by 15.8 times. Our findings highlight the effectiveness and cost-efficiency of visual prompts with MLLMs for various sensory tasks. The source code is available at https://github.com/diamond264/ByMyEyes.

Dingkang Yang, Jinjie Wei, Dongling Xiao et al.

Developing intelligent pediatric consultation systems offers promising prospects for improving diagnostic efficiency, especially in China, where healthcare resources are scarce. Despite recent advances in Large Language Models (LLMs) for Chinese medicine, their performance is sub-optimal in pediatric applications due to inadequate instruction data and vulnerable training procedures. To address the above issues, this paper builds PedCorpus, a high-quality dataset of over 300,000 multi-task instructions from pediatric textbooks, guidelines, and knowledge graph resources to fulfil diverse diagnostic demands. Upon well-designed PedCorpus, we propose PediatricsGPT, the first Chinese pediatric LLM assistant built on a systematic and robust training pipeline. In the continuous pre-training phase, we introduce a hybrid instruction pre-training mechanism to mitigate the internal-injected knowledge inconsistency of LLMs for medical domain adaptation. Immediately, the full-parameter Supervised Fine-Tuning (SFT) is utilized to incorporate the general medical knowledge schema into the models. After that, we devise a direct following preference optimization to enhance the generation of pediatrician-like humanistic responses. In the parameter-efficient secondary SFT phase, a mixture of universal-specific experts strategy is presented to resolve the competency conflict between medical generalist and pediatric expertise mastery. Extensive results based on the metrics, GPT-4, and doctor evaluations on distinct doctor downstream tasks show that PediatricsGPT consistently outperforms previous Chinese medical LLMs. Our model and dataset will be open-source for community development.

Quanyu Long, Yin Wu, Wenya Wang et al.

In-context Learning (ICL) has emerged as a powerful capability alongside the development of scaled-up large language models (LLMs). By instructing LLMs using few-shot demonstrative examples, ICL enables them to perform a wide range of tasks without updating millions of parameters. However, the precise contributions of demonstrations towards improving end-task performance have not been thoroughly investigated in recent analytical studies. In this paper, we empirically decompose the overall performance of ICL into three dimensions, label space, format, and discrimination, and we evaluate four general-purpose LLMs across a diverse range of tasks. Counter-intuitively, we find that the demonstrations have a marginal impact on provoking discriminative knowledge of language models. However, ICL exhibits significant efficacy in regulating the label space and format, which helps LLMs respond to desired label words. We then demonstrate that this ability functions similar to detailed instructions for LLMs to follow. We additionally provide an in-depth analysis of the mechanism of retrieval helping with ICL. Our findings demonstrate that retrieving the semantically similar examples notably boosts the model's discriminative capability. However, we also observe a trade-off in selecting good in-context examples regarding label diversity.

Atsuki Yamaguchi, Terufumi Morishita, Aline Villavicencio et al.

Vocabulary expansion (VE) is the de-facto approach to language adaptation of large language models (LLMs) by adding new tokens and continuing pre-training on target data. While this is effective for base models trained on unlabeled data, it poses challenges for chat models trained to follow instructions through labeled conversation data. Directly adapting the latter with VE on target unlabeled data may result in forgetting chat abilities. While ideal, target chat data is often unavailable or costly to create for low-resource languages, and machine-translated alternatives are not always effective. To address this issue, previous work proposed using a base and chat model from the same family. This method first adapts the base LLM with VE on target unlabeled data and then converts it to a chat model by adding a chat vector (CV) derived from the weight difference between the source base and chat models. We propose ElChat, a new language adaptation method for chat LLMs that adapts a chat model directly on target unlabeled data, without a base model. It elicits chat abilities by injecting information from the source chat model. ElChat offers more robust and competitive target language and safety performance while achieving superior English, chat, and instruction-following abilities compared to CV.

S. Manzello, Sayaka Suzuki, M. Gollner et al.

Large outdoor fires are an increasing danger to the built environment. Wildfires that spread into communities, labeled as Wildland-Urban Interface (WUI) fires, are an example of large outdoor fires. Other examples of large outdoor fires are urban fires including those that may occur after earthquakes as well as in informal settlements. When vegetation and structures burn in large outdoor fires, pieces of burning material, known as firebrands, are generated, become lofted, and may be carried by the wind. This results in showers of wind-driven firebrands that may land ahead of the fire front, igniting vegetation and structures, and spreading the fire very fast. Post-fire disaster studies indicate that firebrand showers are a significant factor in the fire spread of multiple large outdoor fires. The present paper provides a comprehensive literature summary on the role of firebrand mechanisms on large outdoor fire spread. Experiments, models, and simulations related to firebrand generation, lofting, burning, transport, deposition, and ignition of materials are reviewed. Japan, a country that has been greatly influenced by ignition induced by firebrands that have resulted in severe large outdoor fires, is also highlighted here as most of this knowledge remains not available in the English language literature. The paper closes with a summary of the key research needs on this globally important problem.

A. M. Gorbylev

The article considers the nature of mastery in martial arts (bugei) and the method to obtain it according to the treatise by Issai Chozan (1659–1741), Tengu Geijutsu Ron (Discourse of Tengu on the Art [of the Sword], 1729). This text is a unique phenomenon in the martial arts literature of the Edo period. A work written with a mass readership in mind, it was received by martial artists as an epiphany and remains a part of the canon of the Japanese bugei until now. The topic of mind and methods of controlling its state occupies the central place in the treatise. The sections focusing on this topic contain a comprehensive analysis of the empirical, “incorrect” state of mind (shin), which is juxtaposed with the state of “true mind” (shintai).According to one version, these sections were actually written not by Issai Chozan, but by one of the greatest Japanese Confucian scholars of the 17th century, a representative of the Japanese Wang Yangming school, Kumazawa Banzan (1619–1691), which, probably, explains the depth in which the topic of mind is covered. The Tengu Geijutsu Ron persuasively shows that mastery in martial arts is the result of achieving the state of “true mind” (shintai), bringing in the right state the pneuma-ki, mastering the technique of battle, training the body, grasping the “nature” (sei) of the weapon used and obtaining the ability to “follow” this nature. Issei Chozan notes that, in the system “mind – pneuma – body,” mind occupies the top, commanding place, directing the ki, which, in turn, directs the body, but the process of achieving mastery is based on using feedback in this system.

I. V. Avdiushenkova

This article examines the transformation of ancestor worship in the context of socio-political and religious conditions and identifies the characteristics of worship in contemporary Japanese society and the changes in the form of practices and functions of this worship occurring today.Ancestor worship is not a phenomenon unique to traditional societies: in the 21st century Japan, ancestor worship rituals are practiced by a large part of the population. After World War II, Japanese veneration practices underwent significant changes. The post-war modernization and urbanization of Japanese society played a major role in these changes, leading to the breakdown of the traditional family system. The concept of ancestor itself changes: the concept of ancestor tends to expand and begins to extend bilaterally (to both the husband and wife lines). There has been a transition from the “obligatory” concept of an ancestor, which includes all deceased ancestors in the direct line of succession regardless of personal preferences, to an “optional” one, which limits the concept of “ancestor” to close relatives whose memories are dear to the descendant. With the change in the concept of the ancestor, the functions of ancestor veneration also undergo a transformation: the former functions of veneration rituals contributed to the stability of the ie system, while the new ones consist in relieving psychological tensions between the living and the dead and bringing comfort to particular people. There has been a “privatization” of ancestor veneration, i.e., a growing dominance of personal functions in veneration. Diversification of family types, especially pronounced in the first decades of the 21st century, is also reflected in the rites of ancestor veneration: alternatives to traditional funerals and new forms of burial and storage of remains are appearing.The article concludes that, despite changes in the functions and forms of ancestor worship, the place given to the dead in their lives by the living remains invariably important. And the individualization of veneration practices and the undying belief of a large part of the Japanese population in the power of ancestor spirits indicate that the ancestor cult in contemporary Japan is apparently at the next stage of its unfolding, but by no means of extinction.

Wataru Zaitsu, Mingzhe Jin

In the first half of 2023, text-generative artificial intelligence (AI), including ChatGPT, equipped with GPT-3.5 and GPT-4, from OpenAI, has attracted considerable attention worldwide. In this study, first, we compared Japanese stylometric features of texts generated by GPT (-3.5 and -4) and those written by humans. In this work, we performed multi-dimensional scaling (MDS) to confirm the distributions of 216 texts of three classes (72 academic papers written by 36 single authors, 72 texts generated by GPT-3.5, and 72 texts generated by GPT-4 on the basis of the titles of the aforementioned papers) focusing on the following stylometric features: (1) bigrams of parts-of-speech, (2) bigram of postpositional particle words, (3) positioning of commas, and (4) rate of function words. MDS revealed distinct distributions at each stylometric feature of GPT (-3.5 and -4) and human. Although GPT-4 is more powerful than GPT-3.5 because it has more parameters, both GPT (-3.5 and -4) distributions are likely to overlap. These results indicate that although the number of parameters may increase in the future, GPT-generated texts may not be close to that written by humans in terms of stylometric features. Second, we verified the classification performance of random forest (RF) for two classes (GPT and human) focusing on Japanese stylometric features. This study revealed the high performance of RF in each stylometric feature: The RF classifier focusing on the rate of function words achieved 98.1% accuracy. Furthermore the RF classifier focusing on all stylometric features reached 100% in terms of all performance indexes (accuracy, recall, precision, and F1 score). This study concluded that at this stage we human discriminate ChatGPT from human limited to Japanese language.

Hiroaki Yamada, Takenobu Tokunaga, Ryutaro Ohara et al.

This paper presents the first dataset for Japanese Legal Judgment Prediction (LJP), the Japanese Tort-case Dataset (JTD), which features two tasks: tort prediction and its rationale extraction. The rationale extraction task identifies the court's accepting arguments from alleged arguments by plaintiffs and defendants, which is a novel task in the field. JTD is constructed based on annotated 3,477 Japanese Civil Code judgments by 41 legal experts, resulting in 7,978 instances with 59,697 of their alleged arguments from the involved parties. Our baseline experiments show the feasibility of the proposed two tasks, and our error analysis by legal experts identifies sources of errors and suggests future directions of the LJP research.

Franz Nowak, Anej Svete, Li Du et al.

This work investigates the computational expressivity of language models (LMs) based on recurrent neural networks (RNNs). Siegelmann and Sontag (1992) famously showed that RNNs with rational weights and hidden states and unbounded computation time are Turing complete. However, LMs define weightings over strings in addition to just (unweighted) language membership and the analysis of the computational power of RNN LMs (RLMs) should reflect this. We extend the Turing completeness result to the probabilistic case, showing how a rationally weighted RLM with unbounded computation time can simulate any deterministic probabilistic Turing machine (PTM) with rationally weighted transitions. Since, in practice, RLMs work in real-time, processing a symbol at every time step, we treat the above result as an upper bound on the expressivity of RLMs. We also provide a lower bound by showing that under the restriction to real-time computation, such models can simulate deterministic real-time rational PTMs.

Erkan Karabulut, Salvatore F. Pileggi, Paul Groth et al.

Digital Twins (DT) facilitate monitoring and reasoning processes in cyber-physical systems. They have progressively gained popularity over the past years because of intense research activity and industrial advancements. Cognitive Twins is a novel concept, recently coined to refer to the involvement of Semantic Web technology in DTs. Recent studies address the relevance of ontologies and knowledge graphs in the context of DTs, in terms of knowledge representation, interoperability and automatic reasoning. However, there is no comprehensive analysis of how semantic technologies, and specifically ontologies, are utilized within DTs. This Systematic Literature Review (SLR) is based on the analysis of 82 research articles, that either propose or benefit from ontologies with respect to DT. The paper uses different analysis perspectives, including a structural analysis based on a reference DT architecture, and an application-specific analysis to specifically address the different domains, such as Manufacturing and Infrastructure. The review also identifies open issues and possible research directions on the usage of ontologies and knowledge graphs in DTs.