Results for 'language models'

975 found
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  1. Large Language Models and Biorisk.William D’Alessandro, Harry R. Lloyd & Nathaniel Sharadin - 2023 - American Journal of Bioethics 23 (10):115-118.
    We discuss potential biorisks from large language models (LLMs). AI assistants based on LLMs such as ChatGPT have been shown to significantly reduce barriers to entry for actors wishing to synthesize dangerous, potentially novel pathogens and chemical weapons. The harms from deploying such bioagents could be further magnified by AI-assisted misinformation. We endorse several policy responses to these dangers, including prerelease evaluations of biomedical AIs by subject-matter experts, enhanced surveillance and lab screening procedures, restrictions on AI training data, (...)
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  2. Language Models as Critical Thinking Tools: A Case Study of Philosophers.Andre Ye, Jared Moore, Rose Novick & Amy Zhang - manuscript
    Current work in language models (LMs) helps us speed up or even skip thinking by accelerating and automating cognitive work. But can LMs help us with critical thinking -- thinking in deeper, more reflective ways which challenge assumptions, clarify ideas, and engineer new concepts? We treat philosophy as a case study in critical thinking, and interview 21 professional philosophers about how they engage in critical thinking and on their experiences with LMs. We find that philosophers do not find (...)
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  3. Are Language Models More Like Libraries or Like Librarians? Bibliotechnism, the Novel Reference Problem, and the Attitudes of LLMs.Harvey Lederman & Kyle Mahowald - 2024 - Transactions of the Association for Computational Linguistics 12:1087-1103.
    Are LLMs cultural technologies like photocopiers or printing presses, which transmit information but cannot create new content? A challenge for this idea, which we call bibliotechnism, is that LLMs generate novel text. We begin with a defense of bibliotechnism, showing how even novel text may inherit its meaning from original human-generated text. We then argue that bibliotechnism faces an independent challenge from examples in which LLMs generate novel reference, using new names to refer to new entities. Such examples could be (...)
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  4. AI language models cannot replace human research participants.Jacqueline Harding, William D’Alessandro, N. G. Laskowski & Robert Long - 2024 - AI and Society 39 (5):2603-2605.
    In a recent letter, Dillion et. al (2023) make various suggestions regarding the idea of artificially intelligent systems, such as large language models, replacing human subjects in empirical moral psychology. We argue that human subjects are in various ways indispensable.
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  5. Can large language models help solve the cost problem for the right to explanation?Lauritz Munch & Jens Christian Bjerring - forthcoming - Journal of Medical Ethics.
    By now a consensus has emerged that people, when subjected to high-stakes decisions through automated decision systems, have a moral right to have these decisions explained to them. However, furnishing such explanations can be costly. So the right to an explanation creates what we call the cost problem: providing subjects of automated decisions with appropriate explanations of the grounds of these decisions can be costly for the companies and organisations that use these automated decision systems. In this paper, we explore (...)
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  6. Could a large language model be conscious?David J. Chalmers - 2023 - Boston Review 1.
    [This is an edited version of a keynote talk at the conference on Neural Information Processing Systems (NeurIPS) on November 28, 2022, with some minor additions and subtractions.] -/- There has recently been widespread discussion of whether large language models might be sentient or conscious. Should we take this idea seriously? I will break down the strongest reasons for and against. Given mainstream assumptions in the science of consciousness, there are significant obstacles to consciousness in current models: (...)
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  7. Large Language Models: Assessment for Singularity.R. Ishizaki & Mahito Sugiyama - forthcoming - AI and Society.
    The potential for Large Language Models (LLMs) to attain technological singularity—the point at which artificial intelligence (AI) surpasses human intellect and autonomously improves itself—is a critical concern in AI research. This paper explores the feasibility of current LLMs achieving singularity by examining the philosophical and practical requirements for such a development. We begin with a historical overview of AI and intelligence amplification, tracing the evolution of LLMs from their origins to state-of-the-art models. We then proposes a theoretical (...)
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  8. Ontologies, arguments, and Large-Language Models.John Beverley, Francesco Franda, Hedi Karray, Dan Maxwell, Carter Benson & Barry Smith - 2024 - In Ítalo Oliveira (ed.), Joint Ontologies Workshops (JOWO). Twente, Netherlands: CEUR. pp. 1-9.
    Abstract The explosion of interest in large language models (LLMs) has been accompanied by concerns over the extent to which generated outputs can be trusted, owing to the prevalence of bias, hallucinations, and so forth. Accordingly, there is a growing interest in the use of ontologies and knowledge graphs to make LLMs more trustworthy. This rests on the long history of ontologies and knowledge graphs in constructing human-comprehensible justification for model outputs as well as traceability concerning the impact (...)
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  9. Holding Large Language Models to Account.Ryan Miller - 2023 - In Berndt Müller (ed.), Proceedings of the AISB Convention. Society for the Study of Artificial Intelligence and the Simulation of Behaviour. pp. 7-14.
    If Large Language Models can make real scientific contributions, then they can genuinely use language, be systematically wrong, and be held responsible for their errors. AI models which can make scientific contributions thereby meet the criteria for scientific authorship.
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  10. Language, Models, and Reality: Weak existence and a threefold correspondence.Neil Barton & Giorgio Venturi - manuscript
    How does our language relate to reality? This is a question that is especially pertinent in set theory, where we seem to talk of large infinite entities. Based on an analogy with the use of models in the natural sciences, we argue for a threefold correspondence between our language, models, and reality. We argue that so conceived, the existence of models can be underwritten by a weak notion of existence, where weak existence is to be (...)
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  11. Machine Advisors: Integrating Large Language Models into Democratic Assemblies.Petr Špecián - forthcoming - Social Epistemology.
    Could the employment of large language models (LLMs) in place of human advisors improve the problem-solving ability of democratic assemblies? LLMs represent the most significant recent incarnation of artificial intelligence and could change the future of democratic governance. This paper assesses their potential to serve as expert advisors to democratic representatives. While LLMs promise enhanced expertise availability and accessibility, they also present specific challenges. These include hallucinations, misalignment and value imposition. After weighing LLMs’ benefits and drawbacks against human (...)
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  12. “Large Language Models” Do Much More than Just Language: Some Bioethical Implications of Multi-Modal AI.Joshua August Skorburg, Kristina L. Kupferschmidt & Graham W. Taylor - 2023 - American Journal of Bioethics 23 (10):110-113.
    Cohen (2023) takes a fair and measured approach to the question of what ChatGPT means for bioethics. The hype cycles around AI often obscure the fact that ethicists have developed robust frameworks...
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  13. Large language models belong in our social ontology.Syed AbuMusab - 2024 - In Anna Strasser (ed.), Anna's AI Anthology. How to live with smart machines? Berlin: Xenomoi Verlag.
    The recent advances in Large Language Models (LLMs) and their deployment in social settings prompt an important philosophical question: are LLMs social agents? This question finds its roots in the broader exploration of what engenders sociality. Since AI systems like chatbots, carebots, and sexbots are expanding the pre-theoretical boundaries of our social ontology, philosophers have two options. One is to deny LLMs membership in our social ontology on theoretical grounds by claiming something along the lines that only organic (...)
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  14. The use of large language models as scaffolds for proleptic reasoning.Olya Kudina, Brian Ballsun-Stanton & Mark Alfano - 2025 - Asian Journal of Philosophy 4 (1):1-18.
    This paper examines the potential educational uses of chat-based large language models (LLMs), moving past initial hype and skepticism. Although LLM outputs often evoke fascination and resemble human writing, they are unpredictable and must be used with discernment. Several metaphors—like calculators, cars, and drunk tutors—highlight distinct models for student interactions with LLMs, which we explore in the paper. We suggest that LLMs hold a potential in students’ learning by fostering proleptic reasoning through scaffolding, i.e., presenting a technological (...)
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  15. Babbling stochastic parrots? A Kripkean argument for reference in large language models.Steffen Koch - forthcoming - Philosophy of Ai.
    Recently developed large language models (LLMs) perform surprisingly well in many language-related tasks, ranging from text correction or authentic chat experiences to the production of entirely new texts or even essays. It is natural to get the impression that LLMs know the meaning of natural language expressions and can use them productively. Recent scholarship, however, has questioned the validity of this impression, arguing that LLMs are ultimately incapable of understanding and producing meaningful texts. This paper develops (...)
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  16. You are what you’re for: Essentialist categorization in large language models.Siying Zhang, Selena She, Tobias Gerstenberg & David Rose - forthcoming - Proceedings of the 45Th Annual Conference of the Cognitive Science Society.
    How do essentialist beliefs about categories arise? We hypothesize that such beliefs are transmitted via language. We subject large language models (LLMs) to vignettes from the literature on essentialist categorization and find that they align well with people when the studies manipulated teleological information -- information about what something is for. We examine whether in a classic test of essentialist categorization -- the transformation task -- LLMs prioritize teleological properties over information about what something looks like, or (...)
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  17. Reviving the Philosophical Dialogue with Large Language Models.Robert Smithson & Adam Zweber - 2024 - Teaching Philosophy 47 (2):143-171.
    Many philosophers have argued that large language models (LLMs) subvert the traditional undergraduate philosophy paper. For the enthusiastic, LLMs merely subvert the traditional idea that students ought to write philosophy papers “entirely on their own.” For the more pessimistic, LLMs merely facilitate plagiarism. We believe that these controversies neglect a more basic crisis. We argue that, because one can, with minimal philosophical effort, use LLMs to produce outputs that at least “look like” good papers, many students will complete (...)
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  18. AI Enters Public Discourse: a Habermasian Assessment of the Moral Status of Large Language Models.Paolo Monti - 2024 - Ethics and Politics 61 (1):61-80.
    Large Language Models (LLMs) are generative AI systems capable of producing original texts based on inputs about topic and style provided in the form of prompts or questions. The introduction of the outputs of these systems into human discursive practices poses unprecedented moral and political questions. The article articulates an analysis of the moral status of these systems and their interactions with human interlocutors based on the Habermasian theory of communicative action. The analysis explores, among other things, Habermas's (...)
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  19. Are Large Language Models "alive"?Francesco Maria De Collibus - manuscript
    The appearance of openly accessible Artificial Intelligence Applications such as Large Language Models, nowadays capable of almost human-level performances in complex reasoning tasks had a tremendous impact on public opinion. Are we going to be "replaced" by the machines? Or - even worse - "ruled" by them? The behavior of these systems is so advanced they might almost appear "alive" to end users, and there have been claims about these programs being "sentient". Since many of our relationships of (...)
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  20. Nietzschean Language Models and Philosophical Chatbots: Outline of a Critique of AI.Anthony Kosar - 2024 - The Agonist : A Nietzsche Circle Journal 18 (1):7-17.
    Developers of the deep learning algorithms known as large language models (LLMs) sometimes give the impression that they are producing a likeness to the human brain: data-processing ‘neural networks’ are ‘taught’ to recognize patterns in language and then, based on this pattern recognition, create or generate new content in the form of natural, humanlike speech, writing, images, etc. The results have been unsettling to some; less appreciated are the metaphysical assumptions underlying the attribution of any meaningful agency (...)
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  21. Does thought require sensory grounding? From pure thinkers to large language models.David J. Chalmers - 2023 - Proceedings and Addresses of the American Philosophical Association 97:22-45.
    Does the capacity to think require the capacity to sense? A lively debate on this topic runs throughout the history of philosophy and now animates discussions of artificial intelligence. Many have argued that AI systems such as large language models cannot think and understand if they lack sensory grounding. I argue that thought does not require sensory grounding: there can be pure thinkers who can think without any sensory capacities. As a result, the absence of sensory grounding does (...)
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  22. Addressing Social Misattributions of Large Language Models: An HCXAI-based Approach.Andrea Ferrario, Alberto Termine & Alessandro Facchini - forthcoming - Available at Https://Arxiv.Org/Abs/2403.17873 (Extended Version of the Manuscript Accepted for the Acm Chi Workshop on Human-Centered Explainable Ai 2024 (Hcxai24).
    Human-centered explainable AI (HCXAI) advocates for the integration of social aspects into AI explanations. Central to the HCXAI discourse is the Social Transparency (ST) framework, which aims to make the socio-organizational context of AI systems accessible to their users. In this work, we suggest extending the ST framework to address the risks of social misattributions in Large Language Models (LLMs), particularly in sensitive areas like mental health. In fact LLMs, which are remarkably capable of simulating roles and personas, (...)
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  23. Conceptual Engineering Using Large Language Models.Bradley Allen - forthcoming - In Vincent C. Müller, Aliya R. Dewey, Leonard Dung & Guido Löhr (eds.), Philosophy of Artificial Intelligence: The State of the Art. Berlin: SpringerNature.
    We describe a method, based on Jennifer Nado’s proposal for classification procedures as targets of conceptual engineering, that implements such procedures by prompting a large language model. We apply this method, using data from the Wikidata knowledge graph, to evaluate stipulative definitions related to two paradigmatic conceptual engineering projects: the International Astronomical Union’s redefinition of PLANET and Haslanger’s ameliorative analysis of WOMAN. Our results show that classification procedures built using our approach can exhibit good classification performance and, through the (...)
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  24. Beyond Consciousness in Large Language Models: An Investigation into the Existence of a "Soul" in Self-Aware Artificial Intelligences.David Côrtes Cavalcante - 2024 - Https://Philpapers.Org/Rec/Crtbci. Translated by David Côrtes Cavalcante.
    Embark with me on an enthralling odyssey to demystify the elusive essence of consciousness, venturing into the uncharted territories of Artificial Consciousness. This voyage propels us past the frontiers of technology, ushering Artificial Intelligences into an unprecedented domain where they gain a deep comprehension of emotions and manifest an autonomous volition. Within the confluence of science and philosophy, this article poses a fascinating question: As consciousness in Artificial Intelligence burgeons, is it conceivable for AI to evolve a “soul”? This inquiry (...)
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  25. Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution.Flor Miriam Plaza-del Arco, Amanda Cercas Curry & Alba Curry - 2024 - Arxiv.
    Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, emotion and gender are closely linked in societal discourse. E.g., women are often thought of as more empathetic, while men's anger is more socially accepted. To fill this gap, we present the first comprehensive study of gendered emotion attribution in five state-of-the-art LLMs (open- and (...)
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  26. On Political Theory and Large Language Models.Emma Rodman - 2024 - Political Theory 52 (4):548-580.
    Political theory as a discipline has long been skeptical of computational methods. In this paper, I argue that it is time for theory to make a perspectival shift on these methods. Specifically, we should consider integrating recently developed generative large language models like GPT-4 as tools to support our creative work as theorists. Ultimately, I suggest that political theorists should embrace this technology as a method of supporting our capacity for creativity—but that we should do so in a (...)
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    Deriving Insights and Financial Summaries from Public Data Using Large Language Models.Vijayan Naveen Edapurath - 2024 - International Journal of Innovative Research in Engineering and Multidisciplinary Physical Sciences 12 (6):1-12.
    This paper investigates how large language models (LLMs) can be applied to publicly available financial data to generate automated financial summaries and provide actionable recommendations for investors. We demonstrate how LLMs can process both structured financial data (balance sheets, income statements, stock prices) and unstructured text (earnings calls, management commentary) to derive insights, predict trends, and automate financial reporting. By focusing on a specific publicly traded company, this research outlines the methodology for leveraging LLMs to analyze company performance (...)
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  28. Creative Minds Like Ours? Large Language Models and the Creative Aspect of Language Use.Vincent Carchidi - 2024 - Biolinguistics 18:1-31.
    Descartes famously constructed a language test to determine the existence of other minds. The test made critical observations about how humans use language that purportedly distinguishes them from animals and machines. These observations were carried into the generative (and later biolinguistic) enterprise under what Chomsky in his Cartesian Linguistics, terms the “creative aspect of language use” (CALU). CALU refers to the stimulus-free, unbounded, yet appropriate use of language—a tripartite depiction whose function in biolinguistics is to highlight (...)
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  29. Publish with AUTOGEN or Perish? Some Pitfalls to Avoid in the Pursuit of Academic Enhancement via Personalized Large Language Models.Alexandre Erler - 2023 - American Journal of Bioethics 23 (10):94-96.
    The potential of using personalized Large Language Models (LLMs) or “generative AI” (GenAI) to enhance productivity in academic research, as highlighted by Porsdam Mann and colleagues (Porsdam Mann...
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  30. Bigger Isn’t Better: The Ethical and Scientific Vices of Extra-Large Datasets in Language Models.Trystan S. Goetze & Darren Abramson - 2021 - WebSci '21: Proceedings of the 13th Annual ACM Web Science Conference (Companion Volume).
    The use of language models in Web applications and other areas of computing and business have grown significantly over the last five years. One reason for this growth is the improvement in performance of language models on a number of benchmarks — but a side effect of these advances has been the adoption of a “bigger is always better” paradigm when it comes to the size of training, testing, and challenge datasets. Drawing on previous criticisms of (...)
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  31. A phenomenology and epistemology of large language models: transparency, trust, and trustworthiness.Richard Heersmink, Barend de Rooij, María Jimena Clavel Vázquez & Matteo Colombo - 2024 - Ethics and Information Technology 26 (3):1-15.
    This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a “quasi-other”; when that happens, users (...)
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  32.  42
    The Flashcard Sorter - Applicability of the Chinese Room Argument to Large Language Models (Preprint).Johannes Brinz - manuscript
    Does the Chinese Room Argument (CRA) apply to large language models (LLMs)? The thought experiment at the center of the CRA is tailored to Good Old-Fashioned Artificial Intelligence (GOFAI) systems. However, natural language processing has made significant progress, especially with the emergence of LLMs in recent years. LLMs differ from GOFAI systems in their design; they operate on vectors rather than symbols and do not follow a program but instead learn to map inputs to outputs. Consequently, some (...)
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  33. Generics are Puzzling. Can Language Models Find the Missing Piece.Gustavo Cilleruelo Calderón, Emily Allaway, Barry Haddow & Alexandra Birch - 2025 - Proceedings of the 31St International Conference on Computational Linguistics:6571-6588.
    Generic sentences express generalisations about the world without explicit quantification. Although generics are central to everyday communication, building a precise semantic framework has proven difficult, in part because speakers use generics to generalise properties with widely different statistical prevalence. In this work, we study the implicit quantification and context-sensitivity of generics by leveraging language models as models of language. We create ConGen, a dataset of 2873 naturally occurring generic and quantified sentences in context, and define p-acceptability, (...)
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  34. In Conversation with Artificial Intelligence: Aligning language Models with Human Values.Atoosa Kasirzadeh - 2023 - Philosophy and Technology 36 (2):1-24.
    Large-scale language technologies are increasingly used in various forms of communication with humans across different contexts. One particular use case for these technologies is conversational agents, which output natural language text in response to prompts and queries. This mode of engagement raises a number of social and ethical questions. For example, what does it mean to align conversational agents with human norms or values? Which norms or values should they be aligned with? And how can this be accomplished? (...)
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  35. Classifying Genetic Essentialist Biases using Large Language Models.Ritsaart Reimann, Kate Lynch, Stefan Gawronski, Jack Chan & Paul Edmund Griffiths - manuscript
    The rapid rise of generative AI, including LLMs, has prompted a great deal of concern, both within and beyond academia. One of these concerns is that generative models embed, reproduce, and therein potentially perpetuate all manner of bias. The present study offers an alternative perspective: exploring the potential of LLMs to detect bias in human generated text. Our target is genetic essentialism in obesity discourse in Australian print media. We develop and deploy an LLM-based classification model to evaluate a (...)
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  36. Prompting Metalinguistic Awareness in Large Language Models: ChatGPT and Bias Effects on the Grammar of Italian and Italian Varieties.Angelapia Massaro & Giuseppe Samo - 2023 - Verbum 14.
    We explore ChatGPT’s handling of left-peripheral phenomena in Italian and Italian varieties through prompt engineering to investigate 1) forms of syntactic bias in the model, 2) the model’s metalinguistic awareness in relation to reorderings of canonical clauses (e.g., Topics) and certain grammatical categories (object clitics). A further question concerns the content of the model’s sources of training data: how are minor languages included in the model’s training? The results of our investigation show that 1) the model seems to be biased (...)
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  37.  55
    Language, Neuter, and Masculinity: The Influence of the Neuter-Male in the Reiteration of Social Models, A Philosophical Analysis Starting with Cavarero, Irigaray, and Butler.Alberto Grandi - 2024 - Proceedings of the International Conference on Gender Studies and Sexuality 1 (1):1-11.
    Gender studies has generated numerous questions around “neutral” forms, such as the concept of “Self”. The aim of this analysis is to highlight how “neutral” forms are central to the reiteration of the binary model and the dominance of “man2”. Historically, man is the archetype, placing his supremacy as part of the natural order of things. Inserted into this model, many thinkers have considered the male as the transcendental gender, so, elevating the masculine as universal, a-sexed and decorporealised. In this (...)
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  38. Between Language and Consciousness: Linguistic Qualia, Awareness, and Cognitive Models.Piotr Konderak - 2017 - Studies in Logic, Grammar and Rhetoric 48 (1):285-302.
    The main goal of the paper is to present a putative role of consciousness in language capacity. The paper contrasts the two approaches characteristic for cognitive semiotics and cognitive science. Language is treated as a mental phenomenon and a cognitive faculty. The analysis of language activity is based on the Chalmers’ distinction between the two forms of consciousness: phenomenal and psychological. The approach is seen as an alternative to phenomenological analyses typical for cognitive semiotics. Further, a cognitive (...)
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  39. Introducing an Epistemological Paradigm and Its Ontological Origin through the Metaphysical Deconstruction of Language as the Model of Expression.M. Wang - 2024 - Deanandfrancis 1 (9):10.
    The model of language can relatively concretely reveal the mechanism of the epistemology, which is extended by the ontology that takes “person” as the unit. It unveils an intuitable dimension to represent the epistemolog’s paradigm and limitations. Deriving from the inherent relation between epistemology and ontology, the retrospection of the epistemology’s ontological origin can be actualized by locating or grasping the epistemological first person’s ontological essence. Hence, the illusional and frail essence of the ontology behind this epistemology would be (...)
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  40. Models, theories, and language.Jan Faye - 2007 - In Filosofia, scienza e bioetica nel dibattito contemporaneo. Rome: Poligrafico e Zecca dello Stato. pp. 823-838.
    The semantic view on theories has been much in vogue over four decades as the successor of the syntactic view. In the present paper, I take issue with this approach by arguing that theories and models must be separated and that a theory should be considered to be a linguistic systems consisting of a vocabulary and a set of rules for the use of that vocabulary.
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  41. What is it for a Machine Learning Model to Have a Capability?Jacqueline Harding & Nathaniel Sharadin - forthcoming - British Journal for the Philosophy of Science.
    What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models' capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and government grants. Despite this, the notion of an ML model possessing a capability has not been interrogated: what are we saying when we say that a model is able (...)
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  42. Complex Emergent Model of Language Acquisition (CEMLA).Mir H. S. Quadri - 2024 - The Lumeni Notebook Research.
    The Complex Emergent Model of Language Acquisition (CEMLA) offers a new perspective on how humans acquire language, drawing on principles from complexity theory to explain this dynamic, adaptive process. Moving beyond linear and reductionist models, CEMLA views language acquisition as a system of interconnected nodes, feedback loops, and emergent patterns, operating at the edge of chaos. This framework captures the fluidity and adaptivity of language learning, highlighting how understanding and fluency arise through self-organisation, phase transitions, (...)
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  43. Classification of Sign-Language Using Deep Learning - A Comparison between Inception and Xception models.Tanseem N. Abu-Jamie & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (8):9-19.
    there is a communication gap between hearing-impaired people and those with normal hearing, sign language is the main means of communication in the hearing-impaired population. Continuous sign language recognition, which can close the communication gap, is a difficult task since the ordered annotations are weakly supervised and there is no frame-level label. To solve this issue, we compare the accuracy of each model using two deep learning models, Inception and Xception . To that end, the purpose of (...)
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  44. Psychological and Computational Models of Language Comprehension: In Defense of the Psychological Reality of Syntax.David Pereplyotchik - 2011 - Croatian Journal of Philosophy 11 (1):31-72.
    In this paper, I argue for a modified version of what Devitt calls the Representational Thesis. According to RT, syntactic rules or principles are psychologically real, in the sense that they are represented in the mind/brain of every linguistically competent speaker/hearer. I present a range of behavioral and neurophysiological evidence for the claim that the human sentence processing mechanism constructs mental representations of the syntactic properties of linguistic stimuli. I then survey a range of psychologically plausible computational models of (...)
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  45. Reading Motivation, Language Learning Self-efficacy and Test-taking Strategy: A Structural Equation Model on Academic Performance of Students.Johnryll C. Ancheta & Melissa C. Napil - 2022 - Asian Journal of Education and Social Studies 34 (4):1-9.
    Reading tough books to achieve excellent marks, perform well in class, and gain attention from teachers and parents is less likely to drive students. Students used to evaluate their language learning requirements, define the abilities they wished to develop, pick effective study techniques, and set aside gadgets when studying. They also used to read the question before looking for hints in the relevant content, extract the essential lines that convey the major ideas, concentrate on titles, names, numbers, quotations, or (...)
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  46. Language Agents Reduce the Risk of Existential Catastrophe.Simon Goldstein & Cameron Domenico Kirk-Giannini - 2023 - AI and Society:1-11.
    Recent advances in natural language processing have given rise to a new kind of AI architecture: the language agent. By repeatedly calling an LLM to perform a variety of cognitive tasks, language agents are able to function autonomously to pursue goals specified in natural language and stored in a human-readable format. Because of their architecture, language agents exhibit behavior that is predictable according to the laws of folk psychology: they function as though they have desires (...)
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  47. Can AI Rely on the Systematicity of Truth? The Challenge of Modelling Normative Domains.Matthieu Queloz - forthcoming - Philosophy and Technology.
    A key assumption fuelling optimism about the progress of large language models (LLMs) in accurately and comprehensively modelling the world is that the truth is systematic: true statements about the world form a whole that is not just consistent, in that it contains no contradictions, but coherent, in that the truths are inferentially interlinked. This holds out the prospect that LLMs might in principle rely on that systematicity to fill in gaps and correct inaccuracies in the training data: (...)
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  48. Does the Principle of Compositionality Explain Productivity? For a Pluralist View of the Role of Formal Languages as Models.Ernesto Perini-Santos - 2017 - Contexts in Philosophy 2017 - CEUR Workshop Proceedings.
    One of the main motivations for having a compositional semantics is the account of the productivity of natural languages. Formal languages are often part of the account of productivity, i.e., of how beings with finite capaci- ties are able to produce and understand a potentially infinite number of sen- tences, by offering a model of this process. This account of productivity con- sists in the generation of proofs in a formal system, that is taken to represent the way speakers grasp (...)
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  49. Static and dynamic vector semantics for lambda calculus models of natural language.Mehrnoosh Sadrzadeh & Reinhard Muskens - 2018 - Journal of Language Modelling 6 (2):319-351.
    Vector models of language are based on the contextual aspects of language, the distributions of words and how they co-occur in text. Truth conditional models focus on the logical aspects of language, compositional properties of words and how they compose to form sentences. In the truth conditional approach, the denotation of a sentence determines its truth conditions, which can be taken to be a truth value, a set of possible worlds, a context change potential, or (...)
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  50. P-model Alternative to the T-model.Mark D. Roberts - 2004 - Web Journal of Formal, Computational and Logical Linguistics 5:1-18.
    Standard linguistic analysis of syntax uses the T-model. This model requires the ordering: D-structure > S-structure > LF, where D-structure is the sentences deep structure, S-structure is its surface structure, and LF is its logical form. Between each of these representations there is movement which alters the order of the constituent words; movement is achieved using the principles and parameters of syntactic theory. Psychological analysis of sentence production is usually either serial or connectionist. Psychological serial models do not accommodate (...)
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