Hasil untuk "Language and Literature"

A. Morrison, Julie Polisena, D. Husereau et al.

P. Skehan

Shelly F. Greenfield, Shelly F. Greenfield, A. Brooks et al.

B. Laufer, J. Hulstijn

R. Day, J. Bamford, W. Renandya et al.

M. Wetherell, J. Potter

Ashkan Negahban, Jeffrey S. Smith

S. Levinson, Francisco Torreira

The core niche for language use is in verbal interaction, involving the rapid exchange of turns at talking. This paper reviews the extensive literature about this system, adding new statistical analyses of behavioral data where they have been missing, demonstrating that turn-taking has the systematic properties originally noted by Sacks et al. (1974; hereafter SSJ). This system poses some significant puzzles for current theories of language processing: the gaps between turns are short (of the order of 200 ms), but the latencies involved in language production are much longer (over 600 ms). This seems to imply that participants in conversation must predict (or ‘project’ as SSJ have it) the end of the current speaker’s turn in order to prepare their response in advance. This in turn implies some overlap between production and comprehension despite their use of common processing resources. Collecting together what is known behaviorally and experimentally about the system, the space for systematic explanations of language processing for conversation can be significantly narrowed, and we sketch some first model of the mental processes involved for the participant preparing to speak next.

Hayk Avetisyan, Artak Gnuni, Levon Mkrtchyan et al.

The given contribution is devoted to the problem of computer modeling in archaeology. The territory of the Republic of Armenia is chosen as a target zone for investigations, which is considered in the context of historical and cultural developments of the neighboring countries. The chronological range of the given study is the Bronze and Iron Ages (3rd-1st millennia BC). Тhe principles of computer modeling are applicable to the investigation of monumental architecture (fortifications, towers, cairns, kites, kurgans, dolmens), aiming at reconstructing both the complexes of the monuments and the historical landscape.

Игорь Панасюк

Современные сетевые игровые и киберспортивные издания являются наиважнейшим элементом гейминга и одной из главных движущих сил популяризаторов как профессиональной киберспортивной сцены, так и игровой индустрии в целом. Являясь важными составляющими современной медиасистемы, игровые и киберспортивные редакции продолжают активно развивать соответствующие им направления журналистики, затрачивая огромное количество ресурсов на поддержание рейтингов и достижение новых высот. Стремительный технический прогресс влияет на постоянную положительную динамику гейм-индустрии, что, в свою очередь, оказывает прямое влияние на киберспорт. Подобные темпы требуют от игровых и киберспортивных сетевых изданий России новых подходов к созданию, распространению и монетизации контента. Цель данной теоретическо-практической статьи заключается в описании экономической модели современного игрового и киберспортивного сетевого издания.

José Contel, Fiona Pugliese

Gema Polethe García Amén, Maryuri Alexandra Zamora Cusme, María Yarixa Macías Pico

El presente estudio tuvo como objetivo evaluar la gestión administrativa y la calidad del servicio del ECU 911 de la ciudad de Portoviejo, provincia de Manabí, Ecuador, el estudio fue de tipo descriptivo con enfoque mixto, integrando métodos cualitativos y cuantitativos que permitieron obtener una visión integral del funcionamiento del sistema de emergencias. Se aplicaron técnicas como la encuesta con una muestra de 384 ciudadanos, así como la observación directa mediante una guía previamente diseñada y aplicada en las instalaciones del ECU 911, los instrumentos utilizados fueron el cuestionario y la guía de observación, Entre los resultados más notables, se destacó que el servicio es relativamente eficiente, y se percibe al personal como amable; sin embargo, también se reportó dificultades al intentar comunicarse con la central en situaciones de emergencia, y tampoco se recibió seguimiento posterior, la observación permitió identificar fallas cuestionables en la ergonomía del mobiliario, accesibilidad y actualización tecnológica, estos hallazgos reflejan una percepción favorable en términos humanos, pero también ponen en evidencia carencias importantes en infraestructura, comunicación y formación ciudadana, aspectos fundamentales para garantizar una atención oportuna y de calidad para la gestión de emergencias.

Erzsébet Drahota-Szabó

Dieser Aufsatz mit dem provokativen Titel versteht sich als ein kleines Experiment, als eine Fallstudie, die eine weitere wissenschaftliche Diskussion anzuregen beabsichtigt. Die zentrale Frage ist: Wer kommt mit der Übersetzung von Sprichwörtern erfolgreicher zurecht: die professionellen ÜbersetzerInnen oder der ChatGPT? Die Verfasserin vergleicht Textausschnitte aus ungarischen literarischen Werken mit ihren deutschen und englischen Übersetzungen. Es werden jeweils die menschlichen Übersetzungen und diejenigen der generativen künstlichen Intelligenz kritisch analysiert. Für die Untersuchung wurden solche ausgangssprachlichen Texte ausgewählt, die ein Sprichwort enthalten, das textkonstitutiv ist, d. h. über eine Art Übersetzungsrelevanz verfügt. Als Bewertungskriterien bei der Analyse der Übersetzungen gelten vor allem die Fragen: Kann der Mensch oder der ChatGPT die Art der Übersetzungsrelevanz des jeweiligen Sprichwortes „besser“ beurteilen? Wer kann – auf Grundlage der Übersetzungsrelevanz – ein adäquates Übersetzungsverfahren wählen? Überspitzt formuliert geht es um die Frage, wofür oder ob überhaupt bzw. wie lange man ÜbersetzerInnen, d. h. den Menschen, bei der Übersetzung literarischer Werke noch braucht.

Jacqueline Rowe, Mateusz Klimaszewski, Liane Guillou et al.

Large language models increasingly support multiple languages, yet most benchmarks for gender bias remain English-centric. We introduce EuroGEST, a dataset designed to measure gender-stereotypical reasoning in LLMs across English and 29 European languages. EuroGEST builds on an existing expert-informed benchmark covering 16 gender stereotypes, expanded in this work using translation tools, quality estimation metrics, and morphological heuristics. Human evaluations confirm that our data generation method results in high accuracy of both translations and gender labels across languages. We use EuroGEST to evaluate 24 multilingual language models from six model families, demonstrating that the strongest stereotypes in all models across all languages are that women are 'beautiful', 'empathetic' and 'neat' and men are 'leaders', 'strong, tough' and 'professional'. We also show that larger models encode gendered stereotypes more strongly and that instruction finetuning does not consistently reduce gendered stereotypes. Our work highlights the need for more multilingual studies of fairness in LLMs and offers scalable methods and resources to audit gender bias across languages.

René Peinl, Vincent Tischler

Similar to LLMs, the development of vision language models is mainly driven by English datasets and models trained in English and Chinese language, whereas support for other languages, even those considered high-resource languages such as German, remains significantly weaker. In this work we present an analysis of open-weight VLMs on factual knowledge in the German and English language. We disentangle the image-related aspects from the textual ones by analyzing accu-racy with jury-as-a-judge in both prompt languages and images from German and international contexts. We found that for celebrities and sights, VLMs struggle because they are lacking visual cognition of German image contents. For animals and plants, the tested models can often correctly identify the image contents ac-cording to the scientific name or English common name but fail in German lan-guage. Cars and supermarket products were identified equally well in English and German images across both prompt languages.

Tianmai M. Zhang, Neil F. Abernethy

Reference errors, such as citation and quotation errors, are common in scientific papers. Such errors can result in the propagation of inaccurate information, but are difficult and time-consuming to detect, posing a significant challenge to scientific publishing. To support automatic detection of reference errors, this work evaluated the ability of large language models in OpenAI's GPT family to detect quotation errors. Specifically, we prepared an expert-annotated, general-domain dataset of statement-reference pairs from journal articles. Large language models were evaluated in different settings with varying amounts of reference information provided by retrieval augmentation. Our results showed that large language models are able to detect erroneous citations with limited context and without fine-tuning. This study contributes to the growing literature that seeks to utilize artificial intelligence to assist in the writing, reviewing, and publishing of scientific papers. Potential avenues for further improvements in this task are also discussed.

Santiago Arena, Antonio Quintero-Rincón

Specific Language Impairment (SLI) is a disorder that affects communication and can affect both comprehension and expression. This study focuses on effectively detecting SLI in children using transcripts of spontaneous narratives from 1063 interviews. A three-stage cascading pipeline was proposed f. In the first stage, feature extraction and dimensionality reduction of the data are performed using the Random Forest (RF) and Spearman correlation methods. In the second stage, the most predictive variables from the first stage are estimated using logistic regression, which is used in the last stage to detect SLI in children from transcripts of spontaneous narratives using a nearest neighbor classifier. The results revealed an accuracy of 97.13% in identifying SLI, highlighting aspects such as the length of the responses, the quality of their utterances, and the complexity of the language. This new approach, framed in natural language processing, offers significant benefits to the field of SLI detection by avoiding complex subjective variables and focusing on quantitative metrics directly related to the child's performance.

Mikhail Tikhomirov, Daniil Chernyshev

Rapid advancements of large language model (LLM) technologies led to the introduction of powerful open-source instruction-tuned LLMs that have the same text generation quality as the state-of-the-art counterparts such as GPT-4. While the emergence of such models accelerates the adoption of LLM technologies in sensitive-information environments the authors of such models don not disclose the training data necessary for replication of the results thus making the achievements model-exclusive. Since those open-source models are also multilingual this in turn reduces the benefits of training a language specific LLMs as improved inference computation efficiency becomes the only guaranteed advantage of such costly procedure. More cost-efficient options such as vocabulary extension and subsequent continued pre-training are also inhibited by the lack of access to high-quality instruction-tuning data since it is the major factor behind the resulting LLM task-solving capabilities. To address the limitations and cut the costs of the language adaptation pipeline we propose Learned Embedding Propagation (LEP). Unlike existing approaches our method has lower training data size requirements due to minimal impact on existing LLM knowledge which we reinforce using novel ad-hoc embedding propagation procedure that allows to skip the instruction-tuning step and instead implant the new language knowledge directly into any existing instruct-tuned variant. We evaluated four Russian vocabulary adaptations for LLaMa-3-8B and Mistral-7B, showing that LEP is competitive with traditional instruction-tuning methods, achieving performance comparable to OpenChat 3.5 and LLaMa-3-8B-Instruct, with further improvements via self-calibration and continued tuning enhancing task-solving capabilities.

Linying Yang, Vik Shirvaikar, Oscar Clivio et al.

Numerous benchmarks aim to evaluate the capabilities of Large Language Models (LLMs) for causal inference and reasoning. However, many of them can likely be solved through the retrieval of domain knowledge, questioning whether they achieve their purpose. In this review, we present a comprehensive overview of LLM benchmarks for causality. We highlight how recent benchmarks move towards a more thorough definition of causal reasoning by incorporating interventional or counterfactual reasoning. We derive a set of criteria that a useful benchmark or set of benchmarks should aim to satisfy. We hope this work will pave the way towards a general framework for the assessment of causal understanding in LLMs and the design of novel benchmarks.

WANG Sili, ZHANG Ling, YANG Heng, LIU Wei

[Purpose/Significance] Deep learning for language modeling is one of the major methods and advanced technologies to enhance language intelligence of machines at present, which has become an indispensable important technical means for automatic processing and analysis of data resources, and intelligent mining of information and knowledge. However, there are still some difficulties in using deep learning for language modeling for technology development and application service in the library and information science (LIS) field. Therefore, this study systematically reviews and reveals the research progress, technical principles, and development methods of deep learning for language modeling, with the aim at providing reliable theoretical basis and feasible methodological paths for the deep understanding and application of deep learning for language modeling for librarians and fellow practitioners. [Method/Process] The data used in this study were collected from the WOS core database, CNKI literature database, arXiv preprint repository, GitHub open-source software hosting platform and the open resources on the Internet. Based on these data, this paper first systematically investigates the background, basic feature representation algorithms, and representative application development tools of deep learning for language modeling, reveals their dynamic evolution and technical principles, and analyzes the advantages and disadvantages and applicability of each algorithm model and development tool. Second, an in-depth analysis of the possible challenging problems faced by the development and application of deep learning for language modeling was performed, and two strategic approaches to expand their application capabilities were put forward. [Results/Conclusions] The important challenges faced by the application and development of deep learning for language modeling include numerous parameters and difficulties to adjust accuracy, relying on a large amount of accurate training data, difficulties in making changes, and the intellectual property and information security issues. In the future, we will start from two aspects of specific domains and feature engineering to expand and improve the application capabilities of deep learning for language modeling. Specifically, we focus on consideration of the collection and preparation of domain data, selection of model architecture, participation of domain experts, and optimization for specific tasks, in order to ensure that the data source of the model is more reliable and secure, and the application effect is more accurate and practical. Moreover, the strategic methods for feature engineering to expand the application capabilities of deep learning for language modeling include selecting appropriate features, feature pre-processing, feature selection, and feature dimensionality reduction. These strategies can help improve the performance and efficiency of deep learning for language models, making them more suitable for specific tasks or domains. To sum up, LIS institutions should leverage the deep learning for language modeling related technologies, guided by the needs of scientific research and social development, and based on advantages of existing literature data resources and knowledge services; they should carry out innovative professional or vertical domain intelligent knowledge management and application service, and develop technology and systems with independent intellectual property rights, which is their long-term sustainable development path.