Language Research Articles

The Coming Transformative Impact of Large Language Models and Artificial Intelligence on Global Business and Education

Rapid advances in the capabilities of Large Language Models (LLM) as a basis for Artificial Intelligence (AI) applications, and their sudden wide accessibility, have garnered significant attention recently. These technologies (e.g., ChatGPT, BARD), which have the ability to predict and generate human language, have led to excitement and concerns regarding their use in various industries. This paper explores the history of LLM, examines their applications in business and education, and delves into the critical ethical concerns and challenges of these emerging technologies to ensure that their uses are not only effective, but also responsible and equitable.

ALONE WITH MY THOUGHTS: USING NATURAL LANGUAGE PROCESSING TO DISTINGUISH LONELY FROM CALM ALONENESS

Abstract Time spent alone is common in daily life, and particularly in old age; it may at times be experienced as lonely, and at times as calming (positive solitude). The use of machine learning to interpret human language (e.g., natural language processing or NLP) has been growing in psychology, and NLP has been used to distinguish experiences of loneliness from positive solitude in social media data (e.g., tweets). How effective might such NLP techniques be for classifying naturally-occurring aloneness experiences in older adults’ everyday lives? The present study uses 1,546 thought samples collected from 133 adults aged 18-85 (M = 49.6 years; 73% female) at moments when they were alone over a 1-3 week period; participants reported their current thoughts (open-ended) and affective states (lonely, calm) at each assessment. Support vector machines were then used to classify moments of aloneness as being lonely (vs. not lonely) and calm (vs. not calm) based on the words participants used when describing their thoughts. When compared against participants’ actual reported affective states, these classifications achieved up to 67% accuracy for “lonely” and 79% accuracy for “calm”. “Lonely” thought reports included the word “work” more frequently, whereas “calm” thought reports more frequently included the words “reading” and “think/thought”, suggesting individuals engage in quiet or contemplative activities during positive solitude. Age differences therein are also examined. Findings suggest that NLP may be a useful tool for identifying older adults at risk of loneliness (and its negative health implications) by using brief reports of their everyday thoughts.

Biomedical Text Mining for Drug Discovery using Natural Language Processing and Deep Learning.

In the realm of drug discovery, the deluge of biomedical literature poses a formidable challenge for researchers to efficiently extract relevant information. Biomedical text mining, a multidisciplinary field at the intersection of computer science and bioinformatics, has emerged as a pivotal tool to unravel the vast troves of information concealed within scientific literature. This abstract explores the synergy between Natural Language Processing (NLP) and Deep Learning (DL) techniques in the context of biomedical text mining, aiming to expedite the drug discovery process. Natural Language Processing, with its capacity to comprehend and interpret human language, serves as the foundational layer for extracting meaningful insights from biomedical texts. Leveraging advanced NLP algorithms, researchers can identify key entities such as genes, proteins, diseases, and their interactions, providing a comprehensive understanding of the biological landscape. However, the complexity and ambiguity inherent in scientific texts demand a more sophisticated approach. Deep Learning, characterized by neural networks with multiple layers, has proven instrumental in handling intricate patterns and latent relationships within biomedical data. The confluence of NLP and DL not only aids in information extraction but also fosters the creation of predictive models for drug discovery. By training models on vast datasets, researchers can predict potential drug candidates, understand drug-disease associations, and unveil novel therapeutic targets. The collaborative approach presented in this abstract embodies a paradigm shift in drug discovery, where the amalgamation of linguistic comprehension and deep learning prowess enables researchers to navigate the labyrinth of biomedical literature with unprecedented efficiency. As the biomedical landscape continues to evolve, the proposed synergistic framework serves as a beacon for researchers and industry professionals, offering a potent toolkit to harness the wealth of information concealed within textual data, ultimately accelerating the drug discovery pipeline.

<b>Systematic Review of Semantic Analysis Methods</b>

NLP (natural language processing) is a very broad subcategory of computer science that focuses on human language processing in computers. Each different NLP processing technique focuses on different parts of linguistics, with semantics being the main focus of this manuscript. This manuscript aims to utilize a systematic review of several published papers ranging from 1998 to the current day to review and summarize critical analysis regarding the three main models that this paper focuses on: Latent Semantic Analysis (LSA), Explicit Semantic Analysis (ESA), and Neural Network based models. This manuscript compares these models against each other, weighs their advantages and disadvantages, and provides their uses. The selected studies in this review were analyzed and examined to ensure that they meet the quality and standards of the proposed research methodology. The results show that Neural network-based solutions are the most popular Semantic Analysis model in Academia (doubling the number of results of ESA and LSA combined), and they are usually the best in most tasks. However, there are specific scenarios and circumstances in which relying on the older LSA and ESA models could be beneficial.

Transitivity and non-uniform subjecthood in agreement attraction.

Research on human language converges on a view in which a grammatical "subject" is the most saliently encoded entity in mental representation. However, subjecthood is not a syntactically uniform phenomenon. Notably, many languages encode morphological distinctions between subjects of transitive verbs (i.e., verbs that require an object) and subjects of intransitive verbs. We ask how this typological pattern manifests in a language like English (which does not morphologically signal it) by examining the "distinctiveness" of transitive versus intransitive subjects in memory during online sentence processing. We conducted a self-paced reading experiment that tested for "attraction" effects (Dillon et al., Journal of Memory and Language, 69(2), 85-103, 2013; Wagers et al., Journal of Memory and Language, 61, 206-237, 2009) in the processing of subject-verb number agreement. We find that transitive subjects trigger attraction effects, but that these effects are mitigated for intransitive subject attractors (independently of the number of other noun phrases present in the intervening clause). We interpret this as indicating that transitive subjects are less distinctive and therefore less representationally salient than intransitive subjects: This is because a transitive subject must compete with another clause-mate core argument (i.e., a direct object), which draws on resources from the same pool of memory resources. On the other hand, an intransitive subject minimally only competes with a non-core argument (i.e., an oblique noun phrase); this consumes fewer memory resources, leaving the subject to enjoy greater spoils.

The giant leap to humankind

Penny (2009) urges that biologists stress more strongly the biological continuity between great apes and humans. Besides stressing genetic similarities, he suggests that great apes also share psychological capacities, including language and tool use. I argue that he underestimates the psychological differences. There is no evidence that great apes have anything approaching human language, or human technological capacity. A main ingredient of human cognition is recursion, which lifted communication to true combinatorial language and simple tool use to advanced combinatorial technology. Recursion may also explain the combinatorial structure of human memory, imagination, and theory of mind. Part of the key, as Penny recognises, may lie in the trajectory of human brain growth, but there is still much to understand in how micro-tweaking of the genome achieved such dramatic differences between ape and human.

Mother-infant social gaze dynamics relate to infant brain activity and word segmentation

The ‘social brain’, consisting of areas sensitive to social information, supposedly gates the mechanisms involved in human language learning. Early preverbal interactions are guided by ostensive signals, such as gaze patterns, which are coordinated across body, brain, and environment. However, little is known about how the infant brain processes social gaze in naturalistic interactions and how this relates to infant language development. During free-play of 9-month-olds with their mothers, we recorded hemodynamic cortical activity of ´social brain` areas (prefrontal cortex, temporo-parietal junctions) via fNIRS, and micro-coded mother’s and infant’s social gaze. Infants’ speech processing was assessed with a word segmentation task. Using joint recurrence quantification analysis, we examined the connection between infants’ ´social brain` activity and the temporal dynamics of social gaze at intrapersonal (i.e., infant’s coordination, maternal coordination) and interpersonal (i.e., dyadic coupling) levels. Regression modeling revealed that intrapersonal dynamics in maternal social gaze (but not infant’s coordination or dyadic coupling) coordinated significantly with infant’s cortical activity. Moreover, recurrence quantification analysis revealed that intrapersonal maternal social gaze dynamics (in terms of entropy) were the best predictor of infants’ word segmentation. The findings support the importance of social interaction in language development, particularly highlighting maternal social gaze dynamics.

Indicare, rappresentare e dislocare. La Deixis am Phantasma di Karl Bühler e i modi del Discorso Riportato

‘Deixis am phantasma’ or imagination-oriented deixis is a mode of reference, originally described by KarlBühler, that directly brings into play one of the distinctive properties of human language, namely the unlimitedcapacity to dislocate in space and time the referents and situations that are the object of discourse. Sincerepresenting and reporting other people's speech is a discourse situation in which speakers must represent,transpose and dislocate referents, situations, and other people's words, reported speech is a very suitable areain which to observe this particular type of deixis. In this paper, after briefly restating the main points of Bühler'stheory of language, i.e., the two-field theory and the different modes of reference within the deictic field, Idiscuss the three different types of imagination-oriented deixis (as identified by Bühler). I then try to point outwhat I consider to be their systematic correlations with the two main deictic modes of reporting speech (directvs. indirect), while, conversely, noting the unsystematic correlations with that intermediate or hybrid form ofreporting that is free indirect speech.

About a Range of Metaphorical Meanings in the Representation
 of the Phenomenon of «Language» in the Book «Herausmit der Sprache» by A. Thalmayr

The article examines the metaphorical models used by the famous German writer, poet, translator Andreas Thalmayr when describing the German language in his book «Heraus mit der Sprache. Ein bisschen Deutsch für Deutsche, Österreicher, Schweizer und andere Aus- und Inländer». Attention to language as an object of research has increased within the framework of cognitive linguistics, which considers natural human language that reflects all the diversity of the world, on the one hand, as an optimal means of storage, creative interpretation and transmission of human knowledge about the surrounding reality. On the other hand, language is an object of scientific linguistic study. The optimal tool for exploring this most complex phenomenon of human culture is a cognitive metaphor,
 which allows us to comprehend this abstract concept in a more concrete and structured way. In the process of analysis, we identified the following most frequent metaphorical models: language is a person, language is an object of nature,
 language is an object of human activity, language is a treasury, language is a system, language is chaos. The metaphorical representation of language turns out to be associated with various semantic images, which indicates the complexity,
 inconsistency and dynamism of the object of research, the versatility of which can be accounted for by the complexity of its carrier – the man.

School Reform: New Future-Ready Quality Outcomes and Proposed Measures

As we increasingly emphasise the importance of developing future-ready outcomes for learners, we will need to also expand new capabilities to measure such outcomes. AI, big data, and analytics are examples of such new capabilities. Ideation is one of six habits of practice we have identified that will prepare students for the future. In this paper, we present a means to computationally appraise ideation quality as one such capability. We have developed a heuristic to appraise the ideation quality of university student essays using natural language processing, a branch of artificial intelligence concerned with the understanding of human languages. Our heuristic allows for ideation quality to be quickly quantified in the form of an ideation score. So, instead of going about the process blindly, we now have a means to provide a point of reference to allow students to give measured consideration to their ideation. Unlike a learning outcome, a future-ready habit is more of a predisposition. Consequently, it is not coherent with conventional assessments, which rather seek to evaluate than to guide. This heuristic represents an outcome of our evaluation of a new problem space in education and is, at the same time, a novel expansion into a space that exploits new capabilities.

Italian Factive islands are not ‘that weak’

Long-distance dependencies are a major property of human language, deriving from the successive application of movement operations across sentences. Nevertheless, this possibility seems to be restricted by the presence of barriers, which ‘trap’ constituents in more or less 'resistant' syntactic islands. Given this scenario, this paper paper investigates a specific type of island, namely factive complements, which are traditionally considered ‘weak’ islands. The main goal is thus to verify the status of factive islands in Italian, based on original experimental data. Moreover, since island effects have been generally tested against wh-extractions, a secondary goal of the present study is to verify whether other types of A’-movement, such as Focus fronting, can be restricted by island effects as well. The results obtained from two experimental tests, carried out with the collaboration of a total of 310 informants, provide evidence that Italian factive islands seem to lie somewhere between strong and weak islands, qualifying as a sort of ‘intermediate’ island. Indeed, direct objects appear to be less constrained than subjects, indirect objects and adjuncts. Furthermore, the extraction of a Focus constituent from a factive complement lowers the acceptability of the sentence, thus indicating that (factive) islands are indeed sensible to Focus fronting.

Sequence representation as an early step in the evolution of language.

Human language is unique in its compositional, open-ended, and sequential form, and its evolution is often solely explained by advantages of communication. However, it has proven challenging to identify an evolutionary trajectory from a world without language to a world with language, especially while at the same time explaining why such an advantageous phenomenon has not evolved in other animals. Decoding sequential information is necessary for language, making domain-general sequence representation a tentative basic requirement for the evolution of language and other uniquely human phenomena. Here, using formal evolutionary analyses of the utility of sequence representation we show that sequence representation is exceedingly costly and that current memory systems found in animals may prevent abilities necessary for language to emerge. For sequence representation to evolve, flexibility allowing for ignoring irrelevant information is necessary. Furthermore, an abundance of useful sequential information and extensive learning opportunities are required, two conditions that were likely fulfilled early in human evolution. Our results provide a novel, logically plausible trajectory for the evolution of uniquely human cognition and language, and support the hypothesis that human culture is rooted in sequential representational and processing abilities.

Systematic testing of three Language Models reveals low language accuracy, absence of response stability, and a yes-response bias.

Humans are universally good in providing stable and accurate judgments about what forms part of their language and what not. Large Language Models (LMs) are claimed to possess human-like language abilities; hence, they are expected to emulate this behavior by providing both stable and accurate answers, when asked whether a string of words complies with or deviates from their next-word predictions. This work tests whether stability and accuracy are showcased by GPT-3/text-davinci-002, GPT-3/text-davinci-003, and ChatGPT, using a series of judgment tasks that tap on 8 linguistic phenomena: plural attraction, anaphora, center embedding, comparatives, intrusive resumption, negative polarity items, order of adjectives, and order of adverbs. For every phenomenon, 10 sentences (5 grammatical and 5 ungrammatical) are tested, each randomly repeated 10 times, totaling 800 elicited judgments per LM (total n = 2,400). Our results reveal variable above-chance accuracy in the grammatical condition, below-chance accuracy in the ungrammatical condition, a significant instability of answers across phenomena, and a yes-response bias for all the tested LMs. Furthermore, we found no evidence that repetition aids the Models to converge on a processing strategy that culminates in stable answers, either accurate or inaccurate. We demonstrate that the LMs' performance in identifying (un)grammatical word patterns is in stark contrast to what is observed in humans (n = 80, tested on the same tasks) and argue that adopting LMs as theories of human language is not motivated at their current stage of development.

NATURAL LANGUAGE PROCESSING

A branch of computer science and AI called natural language processing is focused on how computers and human languages interact.In order for computers to interpret written and spoken human language without the use of computer-driven language, a type of AI known as natural language processing deals with analysing, understanding, and producing natural human languages. Natural language processing involves the exploration of diverse linguistic features through mathematical and computational modelling, and encompasses the development of various systems. These systems include Verbal language system that merge Verbal communication and Common language. Given that a significant portion of the field concerns the linguistic aspects of computation, natural language processing is considered a part of computer science. Its focus is on utilizing computers to understand and modify written or spoken language for beneficial applications

From Cultured Chats to the Chirrups of Choo-Choo-Da-Choos, or How We Found a Key to the Gate of Eden

Reinvention of the form of expression is a conceptual approach characteristic for the evolution of all arts. This research study provides one such step forward in the advancement of scientific paper, a standard form of expression in natural sciences, toward more progressive terrains. The paper adopts the form of a theatrical play where a scientific family of four attempts to find the way around a writer’s block (Act I). Their idealess sense of confinement is overcome through arts or, more specifically, through scientific research implementing the aesthetics of music as its key element. The characters record compositions differing in the degrees of consonance, rhythmicity and tonality, and play them to birds to observe their vocal and behavioral responses (Act II). They note that the birdsong volume drops significantly when the piano tunes, especially the compositionally complex ones, are played to the birds and increases signifcantly when the white noise of ocean waves is played. The bird count experiment proves indirectly that the birdsong volume reduction is an auditory response of the birds to music rather than the effect of their migrating to or away from the sound source. This science with the flavor of art elevates the characters beyond the limits of their confinement and they, exalted and uplifted, gain ideas for creative research in their own disciplines of biomedical science (Act III). They relinquish the newly gained freedom in favor of embracing love for humanity that underlies medical research and return to their confinement, where they conduct, in the coda of the paper, a series of experiments that venture beyond musicology and ornithology and fall in the domains of tissue engineering and drug delivery, but also psychology (Act III). First, sparrow nests are used as templates for tissue engineering constructs, which facilitate the growth of human fibroblastic cells better than their artificial, gridded replicas. Next, the shell of an Easter egg, predominantly calcite in composition, is used with success as a drug delivery carrier. Lastly, the bird nest drawing experiment demonstrates a greater mental health of children than that of the adults, correlating with one of the overarching points of the paper, which is to present children as a model for grownups to follow rather than the other way around. Over the course of the play, the characters discuss the value of the various fusions between art and science, question the limits of human language and repeatedly go off-topic to celebrate the freeness of the creative thinking process. The play reports on real-life research methods and scientific results through a narrative, without diminishing their empirical rigor and analytical accuracy.

Local similarity and global variability characterize the semantic space of human languages.

How does meaning vary across the world's languages? Scholars recognize the existence of substantial variability within specific domains, ranging from nature and color to kinship. The emergence of large language models enables a systems-level approach that directly characterizes this variability through comparison of word organization across semantic domains. Here, we show that meanings across languages manifest lower variability within semantic domains and greater variability between them, using models trained on both 1) large corpora of native language text comprising Wikipedia articles in 35 languages and also 2) Test of English as a Foreign Language (TOEFL) essays written by 38,500 speakers from the same native languages, which cluster into semantic domains. Concrete meanings vary less across languages than abstract meanings, but all vary with geographical, environmental, and cultural distance. By simultaneously examining local similarity and global difference, we harmonize these findings and provide a description of general principles that govern variability in semantic space across languages. In this way, the structure of a speaker's semantic space influences the comparisons cognitively salient to them, as shaped by their native language, and suggests that even successful bilingual communicators likely think with "semantic accents" driven by associations from their native language while writing English. These findings have dramatic implications for language education, cross-cultural communication, and literal translations, which are impossible not because the objects of reference are uncertain, but because associations, metaphors, and narratives interlink meanings in different, predictable ways from one language to another.

From Cultured Chats to the Chirrups of Choo-Choo-Da-Choos, or How We Found a Key to the Gate of Eden

Reinvention of the form of expression is a conceptual approach characteristic for the evolution of all arts. This research study provides one such step forward in the advancement of scientific paper, a standard form of expression in natural sciences, toward more progressive terrains. The paper adopts the form of a theatrical play where a scientific family of four attempts to find the way around a writer’s block (Act I). Their idealess sense of confinement is overcome through arts or, more specifically, through scientific research implementing the aesthetics of music as its key element. The characters record compositions differing in the degrees of consonance, rhythmicity and tonality, and play them to birds to observe their vocal and behavioral responses (Act II). They note that the birdsong volume drops significantly when the piano tunes, especially the compositionally complex ones, are played to the birds and increases signifcantly when the white noise of ocean waves is played. The bird count experiment proves indirectly that the birdsong volume reduction is an auditory response of the birds to music rather than the effect of their migrating to or away from the sound source. This science with the flavor of art elevates the characters beyond the limits of their confinement and they, exalted and uplifted, gain ideas for creative research in their own disciplines of biomedical science (Act III). They relinquish the newly gained freedom in favor of embracing love for humanity that underlies medical research and return to their confinement, where they conduct, in the coda of the paper, a series of experiments that venture beyond musicology and ornithology and fall in the domains of tissue engineering and drug delivery, but also psychology (Act III). First, sparrow nests are used as templates for tissue engineering constructs, which facilitate the growth of human fibroblastic cells better than their artificial, gridded replicas. Next, the shell of an Easter egg, predominantly calcite in composition, is used with success as a drug delivery carrier. Lastly, the bird nest drawing experiment demonstrates a greater mental health of children than that of the adults, correlating with one of the overarching points of the paper, which is to present children as a model for grownups to follow rather than the other way around. Over the course of the play, the characters discuss the value of the various fusions between art and science, question the limits of human language and repeatedly go off-topic to celebrate the freeness of the creative thinking process. The play reports on real-life research methods and scientific results through a narrative, without diminishing their empirical rigor and analytical accuracy.

The Language Charades: A Review of the Language Game by Morten H. Christiansen and Nick Chater

Language is a tool of expression and communication. This comes naturally to us that we take language for granted. We rarely question ourselves if we can ever survive without a language. How do we learn a language? Why do we learn languages, especially our first language, so fast? These questions have been the center of a contentious and still continuous debate. A debate that brought about several theories that endeavor to explain how the human language became intricate and advanced, yet so easily learnable. Chomsky (1959) claimed that language is an innate ability that is hardwired in our brains. Others, such as Everett (2012), argued that language is a cultural tool that humans invested to satisfy their communicative needs.

Racism in Argentine higher education. Proposals for an inclusive foreign language teacher training.

In Latin America, racism has been a problem since colonial times and is still found in higher education discriminatory practices. Contemporary racism in teacher training institutions and in foreign language career proposals is characterized by the lack of diversity in many aspects such as institutional human landscape, bibliography, language, and cultural uses. All this maintains a higher education that has traditionally produced and still produces a social reality that is non-plural, inequitable, unjust, and in need of revision. This omission of plurality needs to include strategies that link the field of higher education and excluded social groups. To this end, we suggest that service-learning could contribute to an inclusive training of foreign language teachers in an Ecology of Knowledge with other possible and necessary knowledges, ways, voices of diverse groups, so necessary for a non-racist higher education. We believe that overcoming the idea of the individual and alluding to the community, going beyond the doors of the institutions in an exchange of knowledge through practical interventions in the community beyond academicism is a practice that deserves to be further developed in higher education institutions. We propose lines of work to carry out a process of change in our teacher training programs and spaces for the construction of a better society. Service-learning is a feasible path that implies an anti-racist stance and we, the higher education community, have the imagination and qualifications required to generate and undertake this transgressive task of a collaborative and plural construction for a diverse, equitable, and just social reality.

Themes and Knowledge Maps for the Study of the Origins of Human Languages

Themes and Knowledge Maps for the Study of the Origins of Human Languages