Language Research Articles

Q-BENCH: A Benchmark for Multi-modal Foundation Models on Low-level Vision from Single Images to Pairs.

The rapid development of Multi-modality Large Language Models (MLLMs) has navigated a paradigm shift in computer vision, moving towards versatile foundational models. However, evaluating MLLMs in low-level visual perception and understanding remains a yet-to-explore domain. To this end, we design benchmark settings to emulate human language responses related to low-level vision: the low-level visual perception (A1) via visual question answering related to low-level attributes (e.g. clarity, lighting); and the low-level visual description (A2), on evaluating MLLMs for low-level text descriptions. Furthermore, given that pairwise comparison can better avoid ambiguity of responses and has been adopted by many human experiments, we further extend the low-level perception-related questionanswering and description evaluations of MLLMs from single images to image pairs. Specifically, for perception (A1), we carry out the LLVisionQA+ dataset, comprising 2,990 single images and 1,999 image pairs each accompanied by an open-ended question about its low-level features; for description (A2), we propose the LLDescribe+ dataset, evaluating MLLMs for low-level descriptions on 499 single images and 450 pairs. Additionally, we evaluate MLLMs on assessment (A3) ability, i.e. predicting score, by employing a softmax-based approach to enable all MLLMs to generate quantifiable quality ratings, tested against human opinions in 7 image quality assessment (IQA) datasets. With 24 MLLMs under evaluation, we demonstrate that several MLLMs have decent low-level visual competencies on single images, but only GPT-4V exhibits higher accuracy on pairwise comparisons than single image evaluations (like humans). We hope that our benchmark will motivate further research into uncovering and enhancing these nascent capabilities of MLLMs. Datasets will be available at https://github.com/Q-Future/Q-Bench.

Breaking Down the Metrics: A Comparative Analysis of LLM Benchmarks

Due to the fast advancement in large language models (LLMs), the natural language processing (NLP) technology domain has witnessed a massive change. It has brought ground breaking developments in how machines understand and generate human language. However, even with these advancements, it remains hard to objectively compare and evaluate LLMs' performance because various benchmarks and metrics are used in the assessment. Such a method requires this study's writers to conduct a comparative analysis of various benchmark LLMs to explain the viability of different evaluation metrics. Analyzing the recognized standards, including GLUE, SuperGLUE, and SQuAD, reveals weaknesses and potential in the present evaluating systems. It embraces quantitative assessments of model performances and benchmarking and critical evaluations of benchmark designs and their scope. The paper discusses methodological issues in benchmarking research and explains that current measures are insufficient and lack comprehensive evaluation patterns. The conclusions of this research underline the significance of creating fully integrated and efficient reference sets that match the rate of innovation in LLM ability and inform other studies and the overall subsequent advancement of more sophisticated and flexible language models.

INTEGRATING OPTIMAL DEEP LEARNING WITH NATURAL LANGUAGE PROCESSING FOR ARABIC SPAM AND HAM TWEETS RECOGNITION

Natural language processing (NLP) is a domain of artificial intelligence (AI) that concentrates on the communication between human and computer language. Detection of Arabic spam and ham tweets involves leveraging deep learning (DL) models, mainly NLP techniques such as brain-like computing and AI-driven tweets recognition, to mechanically differentiate between spam and ham messages dependent upon content semantics, linguistic patterns, and contextual data within the Arabic text. This study presents an optimal deep learning with natural language processing for Arabic spam and ham tweets recognition (ODLNLP-ASHTR) technique in various complex systems platforms. In the ODLNLP-ASHTR technique, the data pre-processing is initially performed to alter the input tweets into a compatible format, and a BERT word embedding process is used. For Arabic ham and spam tweet recognition, the ODLNLP-ASHTR technique makes use of the self-attention bidirectional gated recurrent unit (SA-BiGRU) model. At last, the detection performance of the SA-BiGRU model can be boosted by the design of an improved salp swarm algorithm (ISSA). The experimental evaluation of the ODLNLP-ASHTR technique takes place using the Arabic tweets dataset. The experimental results pointed out the improved performance of the ODLNLP-ASHTR model compared to recent approaches with a maximum accuracy of 98.11%.

Distributional Semantics: Meaning Through Culture and Interaction.

Mastering how to convey meanings using language is perhaps the main challenge facing any language learner. However, satisfactory accounts of how this is achieved, and even of what it is for a linguistic item to have meaning, are hard to come by. Nick Chater was one of the pioneers involved in the early development of one of the most successful methodologies within the cognitive science of language for discovering meaning: distributional semantics. In this article, we review this approach and discuss its successes and shortcomings in capturing semantic phenomena. In particular, we discuss what we dub the "distributional paradox:" how can models that do not implement essential dimensions of human semantic processing, such as sensorimotor grounding, capture so many meaning-related phenomena? We conclude by providing a preliminary answer, arguing that distributional models capture the statistical scaffolding of human language acquisition that allows for communication, which, in line with Nick Chater's more recent ideas, has been shaped by the features of human cognition on the timescale of cultural evolution.

Advancements and Applications of Large Language Models in Natural Language Processing: A Comprehensive Review

Abstract. Large language models (LLMs) have revolutionized the field of natural language processing (NLP), demonstrating remarkable capabilities in understanding, generating, and manipulating human language. This comprehensive review explores the development, applications, optimizations, and challenges of LLMs. This paper begin by tracing the evolution of these models and their foundational architectures, such as the Transformer, GPT, and BERT. We then delve into the applications of LLMs in natural language understanding tasks, including sentiment analysis, named entity recognition, question answering, and text summarization, highlighting real-world use cases. Next, we examine the role of LLMs in natural language generation, covering areas such as content creation, language translation, personalized recommendations, and automated responses. We further discuss LLM applications in other NLP tasks like text style transfer, text correction, and language model pre-training. Subsequently, we explore techniques for optimizing and improving LLMs, including model compression, explainability, robustness, and security. Finally, we address the challenges posed by the significant computational requirements, sample inefficiency, and ethical considerations surrounding LLMs. We conclude by discussing potential future research directions, such as efficient architectures, few-shot learning, bias mitigation, and privacy-preserving techniques, which will shape the ongoing development and responsible deployment of LLMs in NLP.

An Analysis of Current Situation and Prospect of Natural Language Processing Application in Discourse Analysis

Abstract. Natural Language Processing is a subfield of Artificial Intelligence and an interdisciplinary field of Linguistics and Computer Science. Previous research mainly focuses on the comparison and practical application of natural language processing technology, but the amount of research on its applications in Linguistics is limited. To fill this gap, this article focuses on the current application of technologies in discourse analysis, which is one of the most popular fields in Linguistics. The researcher will initially summarize the existing techniques and tools of Natural Language Processing used for pre-processing, such as Tokenization, Stemming, and Named Entity Recognition. Open-source tools such as NLTK and Spacy for pre-processing are also mentioned in the article. Subsequently, the researcher will introduce commonly used technologies like sentiment analysis and semantic analysis. This research concludes that discourse analysis can benefit many industries such as Medicine, Economics, Agriculture and Politics. However, this paper also analyzes the drawbacks and challenges that NLP technologies for discourse analysis face. Barriers such as Word Sense Disambiguation (WSD) and the context dependency of human language made it difficult for NLP technologies to be applied in Pragmatic analysis.

Humans and great apes visually track event roles in similar ways.

Human language relies on a rich cognitive machinery, partially shared with other animals. One key mechanism, however, decomposing events into causally linked agent-patient roles, has remained elusive with no known animal equivalent. In humans, agent-patient relations in event cognition drive how languages are processed neurally and expressions structured syntactically. We compared visual event tracking between humans and great apes, using stimuli that would elicit causal processing in humans. After accounting for attention to background information, we found similar gaze patterns to agent-patient relations in all species, mostly alternating attention to agents and patients, presumably in order to learn the nature of the event, and occasionally privileging agents under specific conditions. Six-month-old infants, in contrast, did not follow agent-patient relations and attended mostly to background information. These findings raise the possibility that event role tracking, a cognitive foundation of syntax, has evolved long before language but requires time and experience to become ontogenetically available.

AI IN COMMUNICATION

Artificial Intelligence (AI) is changing how we interact, providing new levels of efficiency, personalization, and innovation in communication. From chatbots and virtual assistants to advanced language processing and instant translation, AI tools are transforming both personal and professional communication. Through the use of machine learning algorithms and extensive data sets, AI can interpret and respond to human language with remarkable accuracy. This transformation enhances customer service, streamlines workflows, and bridges language gaps, fostering deeper connections in our increasingly interconnected world. As AI continues to advance, its influence on communication will redefine how information is shared and relationships are built. KEY WORDS: Artificial Intelligence, communication, Real-time translation.

DEVELOPING CROSS-CULTURAL COMPETENCE IN MUSIC TALENT FOR CIVILIZATIONAL EXCHANGE AND MUTUAL LEARNING

With the deepening of globalization, exchanges and mutual learning among different civilizations have become a crucial driving force for promoting global harmony and development. As an art form that transcends human language and culture, music plays a vital role in fostering communication and understanding between diverse civilizations. Against the backdrop of civilizational exchange and mutual learning, this paper explores the importance and necessity of cultivating cross-cultural competence in music talent. It further investigates implementation pathways and strategies, aiming to provide valuable insights and references for the field of music education. KEYWORDS: Civilizational Exchange and Mutual Learning; Music Talent; Cross-Cultural Understanding; Global Landscape; Musical Cultural Literacy

Review of Language Structures and NLP Techniques for Chinese, Japanese, and English

The increasing demand for cross-lingual communication in a globally interconnected world has spurred significant advancements in Natural Language Processing (NLP), a branch of artificial intelligence aimed at enabling computers to comprehend, generate, and process human language. Over the past few years, NLP has transitioned from traditional rule-based and statistical approaches to deep learning-based methods, with pre-trained models like BERT and GPT demonstrating remarkable performance across a variety of tasks, including text classification and machine translation. However, the linguistic diversity of the world's languages presents ongoing challenges, as many NLP models are primarily developed for high-resource languages like English, leaving other languages underserved. This paper examines the linguistic features of three representative languagesChinese, Japanese, and Englishand explores NLP methodologies tailored to their distinct grammatical, lexical, and structural properties. Through comparative linguistic analysis, this study discusses the challenges of multilingual NLP and the potential for cross-lingual applications. The findings underscore the importance of adapting NLP models to different language characteristics to enhance the effectiveness and inclusivity of cross-lingual processing.

Clinical, Research, and Educational Applications of ChatGPT in Dentistry: A Narrative Review

Artificial intelligence (AI), specifically Generative Pre-trained Transformer (GPT) technology, has revolutionized various fields, including medicine and dentistry. The AI model ChatGPT, developed by OpenAI, mimics human language on a large scale, generating coherent and contextually appropriate responses. ChatGPT serves as an auxiliary resource for diagnosis and decision-making across various medical disciplines. This comprehensive narrative review aims to explore how ChatGPT can assist the dental sector, highlighting its potential to enhance various aspects of the discipline. This review includes a literature search on the application of ChatGPT in dentistry, with a focus on the differences between the free version, ChatGPT 3.5, and the more advanced subscription-based version, ChatGPT 4. Specifically, ChatGPT has proven to be effective in enhancing user interaction, providing fast and accurate information and improving the accessibility of knowledge. However, despite these advantages, several limitations are identified, including concerns regarding the accuracy of responses in complex scenarios, ethical considerations surrounding its use, and the need for improved training to handle highly specialized queries. In conclusion, while ChatGPT offers numerous benefits in terms of efficiency and scalability, further research and development are needed to address these limitations, particularly in areas requiring greater precision, ethical oversight, and specialized expertise.

The shared genetic architecture and evolution of human language and musical rhythm.

This study aimed to test theoretical predictions over biological underpinnings of previously documented phenotypic correlations between human language-related and musical rhythm traits. Here, after identifying significant genetic correlations between rhythm, dyslexia and various language-related traits, we adapted multivariate methods to capture genetic signals common to genome-wide association studies of rhythm (N = 606,825) and dyslexia (N = 1,138,870). The results revealed 16 pleiotropic loci (P < 5 × 10-8) jointly associated with rhythm impairment and dyslexia, and intricate shared genetic and neurobiological architectures. The joint genetic signal was enriched for foetal and adult brain cell-specific regulatory regions, highlighting complex cellular composition in their shared underpinnings. Local genetic correlation with a key white matter tract (the left superior longitudinal fasciculus-I) substantiated hypotheses about auditory-motor connectivity as a genetically influenced, evolutionarily relevant neural endophenotype common to rhythm and language processing. Overall, we provide empirical evidence of multiple aspects of shared biology linking language and musical rhythm, contributing novel insight into the evolutionary relationships between human musicality and linguistic communication traits.

Erratological model of teaching Russian to foreign non-philologist

Importance. In the modern world, there is a continuous search for new approaches to teaching students the Russian language. This problem is especially relevant when it comes to foreign stu dents of technical universities who must not only get a profession in Russian but also successfully adapt to Russian society. The purpose of the study is to develop an authorial (erratological) model of teaching the Russian language to non-philological foreigners. Its structural and meaningful de scription is proposed.Materials and methods. The base for the experimental research is the Military Institute (engineer ing and technical) of the Military Academy of Logistics and Technical Support named after. Army General A.V. Khruleva. The research material is based on a corpus of errors identified in the proc ess of analysing the non-philology students’ speech. The key research method is the error analysis method.Results and Discussion. The research result is the development of an erratological teaching model based on a nine-stage error analysis system, which includes recognition of erroneous activity – re cording the fact of deviation – identification of errors – formulation of grammatical rules instructions – correction (elimination) of errors – control and correction activities (training) – for mation self-control skills – fixation of error-free (correct) speech activity – reflection of acquired knowledge.Conclusion. The results obtained suggest that the use of the author’s conceptual model of peda gogical influence in classes for a foreign audience will not only develop Russian-speaking profes sional competence but also ensure the undoubted foreign student’s interest in learning a new lan guage and will also contribute to the intensification of the educational process, enriching it with new technologies, teaching methods, and a new system of tasks.

Infographic text as a tool for organizing Russian-language educational discourse

Importance. The significance of the subject matter arises from the potential for organizing effec tive foreign language communication based on contemporary methods of educational materials’ visual representation, one of which is infographic – a relevant, authentic material that presents in formation in a concise and comprehensible form, integrating verbal and non-verbal elements.Materials and Methods. The following methods are used: analysis of academic research on the infographic text application in teaching foreign languages and practical experience generalization with the use of such materials in Russian as a foreign language. The research materials included academic papers that provided methodological justification for the infographics use as well as infographic materials available online and personal experience gained from utilizing infographic texts within the educational process.Results and Discussion. Based on the material analysis results on the topic, it is concluded that there is a significant potential for using infographic text with supporting structures in Russian as a foreign language teaching. In this regard, the theoretical knowledge necessary for a teacher to or ganize productive speech activity in a foreign language is described: the main stages of organizing work with infographics, the requirements for the of infographics content are listed.Conclusions. Infographic texts are valuable methodological material in teaching foreign lan guages, around which a foreign language educational discourse is built. Methodically, the tradi tional pre-text, pre-text and post-text tasks for working with the educational text should be orga nized competently. Here, support structures are important, which stimulate speech activity, con tributing to the effective work organization on the oral and written speech production based on a given algorithm.

Does ChatGPT have sociolinguistic competence?

Large language models are now able to generate content- and genre-appropriate prose with grammatical sentences. However, these targets do not fully encapsulate human-like language use. For example, set aside is the fact that human language use involves sociolinguistic variation that is regularly constrained by internal and external factors. This article tests whether one widely used LLM application, ChatGPT, is capable of generating such variation. I construct an English corpus of “sociolinguistic interviews” using the application and analyze the generation of seven morphosyntactic features. I show that the application largely fails to generate any variation at all when one variant is prescriptively incorrect, but that it is able to generate variable deletion of the complementizer that that is internally constrained, with variants occurring at human-like rates. ChatGPT fails, however, to properly generate externally constrained complementizer that deletion. I argue that these outcomes reflect bias both in the training data and Reinforcement Learning from Human Feedback. I suggest that testing whether an LLM can properly generate sociolinguistic variation is a useful metric for evaluating if it generates human-like language.

The Geometry and Dynamics of Meaning.

An enigma for human languages is that children learn to understand words in their mother tongue extremely fast. The cognitive sciences have not been able to fully understand the mechanisms behind this highly efficient learning process. In order to provide at least a partial answer to this problem, I have developed a cognitive model of the semantics of natural language in terms of conceptual spaces. I present a background to conceptual spaces and provide a brief summary of their main features, in particular how it handles learning of concepts. I then apply the model to give a geometric account of the semantics of different word classes. In particular, I propose a "single-domain hypotheses" for the semantics of all word classes except nouns. These hypotheses provide a partial answer to the enigma of how words are learned. Next, a dynamic cognitive model of events is introduced that replaces and extends the function of thematic roles. I apply it to analyze the meanings of different kinds of verbs. I argue that the model also explains some aspects of syntactic structure. In particular, I propose that a sentence typically refers to an event. Some further applications of conceptual spaces are briefly presented.

Unique cortical and subcortical activation patterns for different conspecific calls in marmosets.

The common marmoset (Callithrix jacchus) is known for its highly vocal nature, displaying a diverse range of calls. Functional imaging in marmosets has shown that the processing of conspecific calls activates a brain network that includes fronto-temporal areas. It is currently unknown whether different call types activate the same or different networks. In this study, nine adult marmosets (four females) were exposed to four common vocalizations (phee, chatter, trill, and twitter), and their brain responses were recorded using event-related fMRI at 9.4T. We found robust activations in the auditory cortices, encompassing core, belt, and parabelt regions, and in subcortical areas like the inferior colliculus, medial geniculate nucleus, and amygdala in response to these calls. Although a common network was engaged, distinct activity patterns were evident for different vocalizations that could be distinguished by a 3D convolution neural network, indicating unique neural processing for each vocalization. Our findings also indicate the involvement of the cerebellum and medial prefrontal cortex (mPFC) in distinguishing particular vocalizations from others.Significance Statement This study investigates the neural processing of vocal communications in the common marmoset (Callithrix jacchus). Utilizing event-related fMRI at 9.4T, we demonstrate that different calls (phee, chatter, trill, and twitter) elicit distinct brain activation patterns, challenging the notion of a uniform neural network for all vocalizations. Each call type distinctly engages various regions within the auditory cortices and subcortical areas. These findings offer insights into the evolutionary mechanisms of primate vocal perception and provide a foundation for understanding the origins of human speech and language processing.

First-language acquisition in a multimodal language framework: Insights from speech, gesture, and sign

Children across the world acquire their first language(s) naturally, regardless of typology or modality (e.g. sign or spoken). Various attempts have been made to explain the puzzle of language acquisition using several approaches, trying to understand to what extent it can be explained by what children bring to language-learning situations as well as what they learn from the input and the interactive context. However, most of these approaches consider only speech development, thus ignoring the inherently multimodal nature of human language. As a multimodal view of language is becoming more widely adopted for the study of adult language, a multimodal approach to language acquisition is inevitable. Not only do children have the capacity to learn spoken and sign language equally easily, but spoken language acquisition consists of learning to coordinate linguistic expressions in both modalities, that is, in both speech and gesture. To provide a step forward in this direction, this article aims to synthesize findings from research studies that take a multimodal perspective on language acquisition in different sign and spoken languages, including the development of speech and accompanying gestures. Our review shows that while some aspects of language acquisition seem to be modality-independent, others might differ according to the affordances of each modality when used separately as well as together (either in sign, speech, and/or gesture). We argue that these findings need to be integrated into our understanding of language acquisition. We also identify which areas need future research for both spoken and sign language acquisition, taking into account not only multimodal but also cross-linguistic variation.

Understanding public opinion and discussion dynamics of digital humans on social media: an analysis of sentiment, themes and user characteristics

PurposeThe emergence of artificial intelligence-generated content (AIGC) technology has markedly enhanced the capabilities of digital human content generation and natural language processing, thus further advancing the development of digital humans. To enable enterprises and governments to effectively address the challenges and opportunities arising from the rapid development of digital humans, it is imperative to understand the public opinion and discussion dynamics of digital humans.Design/methodology/approachThis study initially analyzed the trends and distribution patterns of public attention to digital humans. By utilizing word cloud technology, we explored the primary focal points of public interest and conducted a topic analysis using latent Dirichlet allocation (LDA) techniques. Subsequently, content analysis was conducted on the popular application domains of digital humans. Finally, this study examined the influence of user characteristics on emotional scores toward digital humans and the presence of differences in focus across user groups.FindingsThe results indicate a sustained increase in public attention toward digital humans, accompanied by notable geographic disparities in the distribution of discussions. Discussions on Weibo are primarily focused on four domains, whereas areas within the digital human application domain that provoke widespread discussion include live streaming, service, cultural entertainment and digital avatars. Significant impacts of user characteristics on sentiment scores were observed, revealing divergent focal points of interest among different user groups toward digital humans.Originality/valueThrough the deep analysis of Weibo data, this study offers new insights into the digital human industry, enabling governments and businesses to understand industry trends and develop targeted digital human customization strategies based on customer characteristics.

Nature and nurture in fruit fly hearing.

As for human language learning and birdsong acquisition, fruit flies adjust their auditory perception based on past sound experiences. This phenomenon is known as song preference learning in flies. Recent advancements in omics databases, such as the single-cell transcriptome and brain connectomes, have been integrated into traditional molecular genetics, making the fruit fly an outstanding model for studying the neural basis of "Nature and Nurture" in auditory perception and behaviors. This minireview aims to provide an overview of song preference in flies, including the nature of the phenomenon and its underlying neural mechanisms. Specifically, we focus on the neural circuitry involved in song preference learning, with which auditory experiences shape the song preference of flies. This shaping process depends on an integration hub that processes external sensory stimuli and internal states to enable flexible control of behavior. We also briefly review recent findings on the signals that feed into this integration hub, modulating song preference of flies in an experience-dependent manner.