Bibliografie tematiche / Large language models

Letteratura scientifica selezionata sul tema "Large language models"

Autore: Grafiati

Pubblicato: 8 giugno 2024

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Articoli di riviste sul tema "Large language models":

Cerf, Vinton G. "Large Language Models". Communications of the ACM 66, n. 8 (25 luglio 2023): 7. http://dx.doi.org/10.1145/3606337.

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Sharma Shria Verma, Dhananjai. "Automated Penetration Testing using Large Language Models". International Journal of Science and Research (IJSR) 13, n. 4 (5 aprile 2024): 1826–31. http://dx.doi.org/10.21275/sr24427043741.

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Mishra, Vinaytosh. "Large Language Models in Medical Education and Quality Concerns". Journal of Quality in Health Care & Economics 6, n. 1 (2023): 1–3. http://dx.doi.org/10.23880/jqhe-16000319.

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Jain, Migul. "Future of Interacting with Computers and Large Language Models". International Journal of Science and Research (IJSR) 12, n. 10 (5 ottobre 2023): 1711–12. http://dx.doi.org/10.21275/sr231023121603.

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Noever, David. "LARGE LANGUAGE MODELS FOR CIPHERS". International Journal of Artificial Intelligence & Applications 14, n. 03 (28 maggio 2023): 1–20. http://dx.doi.org/10.5121/ijaia.2023.14301.

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Abstract (sommario):

This study investigates whether transformer models like ChatGPT (GPT4, MAR2023) can generalize beyond their training data by examining their performance on the novel Cipher Dataset, which scrambles token order. The dataset consists of 654 test cases, and the analysis focuses on 51 text examples and 13 algorithmic choices. Results show that the models perform well on low-difficulty ciphers like Caesar and can unscramble tokens in 77% of the cipher examples. Despite their reliance on training data, the model's ability to generalize outside of token order is surprising, especially when leveraging large-scale models with hundreds of billions of weights and a comprehensive text corpus with few examples. The original contributions of the work focus on presenting a cipher challenge dataset and then scoring historically significant ciphers for large language models to descramble. The real challenge for these generational models lies in executing the complex algorithmic steps on new cipher inputs, potentially as a novel reasoning challenge that relies less on knowledge acquisition and more on trial-and-error or out-ofbounds responses.

D’Alessandro, William, Harry R. Lloyd e Nathaniel Sharadin. "Large Language Models and Biorisk". American Journal of Bioethics 23, n. 10 (3 ottobre 2023): 115–18. http://dx.doi.org/10.1080/15265161.2023.2250333.

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Shanahan, Murray. "Talking about Large Language Models". Communications of the ACM 67, n. 2 (25 gennaio 2024): 68–79. http://dx.doi.org/10.1145/3624724.

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Interacting with a contemporary LLM-based conversational agent can create an illusion of being in the presence of a thinking creature. Yet, in their very nature, such systems are fundamentally not like us.

Cheon, Hyundeuk. "Do Large Language Models Understand?" CHUL HAK SA SANG : Journal of Philosophical Ideas 90 (30 novembre 2023): 75–105. http://dx.doi.org/10.15750/chss.90.202311.003.

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Veres, Csaba. "Large Language Models are Not Models of Natural Language: They are Corpus Models". IEEE Access 10 (2022): 61970–79. http://dx.doi.org/10.1109/access.2022.3182505.

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Ross, Angela, Kathleen McGrow, Degui Zhi e Laila Rasmy. "Foundation Models, Generative AI, and Large Language Models". CIN: Computers, Informatics, Nursing 42, n. 5 (maggio 2024): 377–87. http://dx.doi.org/10.1097/cin.0000000000001149.

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We are in a booming era of artificial intelligence, particularly with the increased availability of technologies that can help generate content, such as ChatGPT. Healthcare institutions are discussing or have started utilizing these innovative technologies within their workflow. Major electronic health record vendors have begun to leverage large language models to process and analyze vast amounts of clinical natural language text, performing a wide range of tasks in healthcare settings to help alleviate clinicians' burden. Although such technologies can be helpful in applications such as patient education, drafting responses to patient questions and emails, medical record summarization, and medical research facilitation, there are concerns about the tools' readiness for use within the healthcare domain and acceptance by the current workforce. The goal of this article is to provide nurses with an understanding of the currently available foundation models and artificial intelligence tools, enabling them to evaluate the need for such tools and assess how they can impact current clinical practice. This will help nurses efficiently assess, implement, and evaluate these tools to ensure these technologies are ethically and effectively integrated into healthcare systems, while also rigorously monitoring their performance and impact on patient care.

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Tesi sul tema "Large language models":

Labeau, Matthieu. "Neural language models : Dealing with large vocabularies". Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS313/document.

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Le travail présenté dans cette thèse explore les méthodes pratiques utilisées pour faciliter l'entraînement et améliorer les performances des modèles de langues munis de très grands vocabulaires. La principale limite à l'utilisation des modèles de langue neuronaux est leur coût computationnel: il dépend de la taille du vocabulaire avec laquelle il grandit linéairement. La façon la plus aisée de réduire le temps de calcul de ces modèles reste de limiter la taille du vocabulaire, ce qui est loin d'être satisfaisant pour de nombreuses tâches. La plupart des méthodes existantes pour l'entraînement de ces modèles à grand vocabulaire évitent le calcul de la fonction de partition, qui est utilisée pour forcer la distribution de sortie du modèle à être normalisée en une distribution de probabilités. Ici, nous nous concentrons sur les méthodes à base d'échantillonnage, dont le sampling par importance et l'estimation contrastive bruitée. Ces méthodes permettent de calculer facilement une approximation de cette fonction de partition. L'examen des mécanismes de l'estimation contrastive bruitée nous permet de proposer des solutions qui vont considérablement faciliter l'entraînement, ce que nous montrons expérimentalement. Ensuite, nous utilisons la généralisation d'un ensemble d'objectifs basés sur l'échantillonnage comme divergences de Bregman pour expérimenter avec de nouvelles fonctions objectif. Enfin, nous exploitons les informations données par les unités sous-mots pour enrichir les représentations en sortie du modèle. Nous expérimentons avec différentes architectures, sur le Tchèque, et montrons que les représentations basées sur les caractères permettent l'amélioration des résultats, d'autant plus lorsque l'on réduit conjointement l'utilisation des représentations de mots
This work investigates practical methods to ease training and improve performances of neural language models with large vocabularies. The main limitation of neural language models is their expensive computational cost: it depends on the size of the vocabulary, with which it grows linearly. Despite several training tricks, the most straightforward way to limit computation time is to limit the vocabulary size, which is not a satisfactory solution for numerous tasks. Most of the existing methods used to train large-vocabulary language models revolve around avoiding the computation of the partition function, ensuring that output scores are normalized into a probability distribution. Here, we focus on sampling-based approaches, including importance sampling and noise contrastive estimation. These methods allow an approximate computation of the partition function. After examining the mechanism of self-normalization in noise-contrastive estimation, we first propose to improve its efficiency with solutions that are adapted to the inner workings of the method and experimentally show that they considerably ease training. Our second contribution is to expand on a generalization of several sampling based objectives as Bregman divergences, in order to experiment with new objectives. We use Beta divergences to derive a set of objectives from which noise contrastive estimation is a particular case. Finally, we aim at improving performances on full vocabulary language models, by augmenting output words representation with subwords. We experiment on a Czech dataset and show that using character-based representations besides word embeddings for output representations gives better results. We also show that reducing the size of the output look-up table improves results even more

Zervakis, Georgios. "Enriching large language models with semantic lexicons and analogies". Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0039.

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Les progrès récents de l'apprentissage profond et des réseaux de neurones ont permis d'aborder des tâches complexes de traitement du langage naturel, qui sont appliquées à une pléthore de problèmes réels allant des assistants intelligents dans les appareils mobiles à la prédiction du cancer. Néanmoins, les systèmes modernes basés sur ces approches présentent plusieurs limitations qui peuvent compromettre leurs performances et leur fiabilité, les rendre injustes envers les minorités ou exposer des données personnelles. Nous sommes convaincus que l'intégration de connaissances et de raisonnement symboliques dans le cadre de l'apprentissage profond est une étape nécessaire vers la résolution de ces limitations. Par exemple, les ressources lexicales peuvent enrichir les réseaux de neurones profonds avec des connaissances sémantiques ou syntaxiques, et les règles logiques peuvent fournir des mécanismes d'apprentissage et de raisonnement. Par conséquent, l'objectif de cette thèse est de développer et d'évaluer des moyens d'intégrer différents types de connaissances et de raisonnement symboliques dans un modèle de langage largement utilisé, le Bidirectional Encoder R presentations from Transformers (BERT). Dans un premier temps, nous considérons le retrofitting, une technique simple et populaire pour raffiner les plongements lexicaux de mots grâce à des relations provenant d'un lexique sémantique. Nous présentons deux méthodes inspirées par cette technique pour incorporer ces connaissances dans des plongements contextuels de BERT. Nous évaluons ces méthodes sur trois jeux de données biomédicales pour l'extraction de relations et un jeu de données de critiques de films pour l'analyse des sentiments, et montrons qu'elles n'ont pas d'impact substantiel sur les performances pour ces tâches. En outre, nous effectuons une analyse qualitative afin de mieux comprendre ce résultat négatif. Dans un second temps, nous intégrons le raisonnement analogique à BERT afin d'améliorer ses performances sur la tâche de vérification du sens d'un mot, et de le rendre plus robuste. Pour cela, nous reformulons la vérification du sens d'un mot comme une tâche de détection d'analogie. Nous présentons un modèle hybride qui combine BERT pour encoder les données d'entrée en quadruplets et un classifieur neuronal convolutif pour décider s'ils constituent des analogies valides. Nous testons notre système sur un jeu de données de référence et montrons qu'il peut surpasser les approches existantes. Notre étude empirique montre l'importance de l'encodage d'entrée pour BERT, et comment cette dépendance est atténuée en intégrant les propriétés axiomatiques des analogies lors de l'apprentissage, tout en préservant les performances et en améliorant la robustesse
Recent advances in deep learning and neural networks have made it possible to address complex natural language processing tasks, which find application in a plethora of real-world problems ranging from smart assistants in mobile devices to the prediction of cancer. Nonetheless, modern systems based on these frameworks exhibit various limitations that may compromise their performance and trustworthiness, render them unfair towards minorities, or subject them to privacy leakage. It is our belief that integrating symbolic knowledge and reasoning into the deep learning framework is a necessary step towards addressing the aforementioned limitations. For example, lexical resources can enrich deep neural networks with semantic or syntactic knowledge, and logical rules can provide learning and reasoning mechanisms. Therefore, the scope of this thesis is to develop and evaluate ways of integrating different types of symbolic knowledge and reasoning into a widely used language model, Bidirectional Encoder Representations from Transformers (BERT). ln a first stage, we consider retrofitting, a simple and popular technique for refining distributional word embeddings based on relations coming from a semantic lexicon. Inspired by this technique, we present two methods for incorporating this knowledge into BERT contextualized embeddings. We evaluate these methods on three biomedical datasets for relation extraction and one movie review dataset for sentiment analysis, and show that they do not substantially impact the performance for these tasks. Furthermore, we conduct a qualitative analysis to provide further insights on this negative result. ln a second stage, we integrate analogical reasoning with BERT as a means to improve its performance on the target sense verification task, and make it more robust. To do so, we reformulate target sense verification as an analogy detection task. We present a hybrid model that combines BERT to encode the input data into quadruples and a convolutional neural classifier to decide whether they constitute valid analogies. We test our system on a benchmark dataset, and show that it can outperform existing approaches. Our empirical study shows the importance of the input encoding for BERT, and how this dependence gets alleviated by integrating the axiomatic properties of analogies during training, while preserving performance and improving robustness

Chadha, Vikrampal. "Simulation of large-scale system-level models". Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-12162009-020334/.

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Kropff, Emilio. "Statistical and dynamical properties of large cortical network models: insights into semantic memory and language". Doctoral thesis, SISSA, 2007. http://hdl.handle.net/20.500.11767/4639.

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Abstract (sommario):

This thesis introduces several variants to the classical autoassociative memory model in order to capture different characteristics of large cortical networks, using semantic memory as a paradigmatic example in which to apply the results. Chapter 2 is devoted to the development of the sparse Potts model network as a simplification of a multi modular memory performing computations both at the local and the global level. If a network storing p global patterns has N local modules, each one active in S possible ways with a global sparseness a, and if each module is connected to cM other modules, the storage capacity scales like αc ≡ pmax /cM ∝ S 2 /a with logarithmic corrections. Chapter 3 further introduces adaptation and correlations among patterns, as a result of which a latching dynamics appears, consistent in the spontaneous hopping between global attractor states after an initial cue-guided retrieval, somehow similar to a free association process. The complexity of the latching series depends on the equilibrium between self-excitation of the local networks and global inhibition represented by the parameter U. Finally, Chapter 4 develops a consistent way to store and retrieve correlated patterns, which works as long as any statistical dependence between units can be neglected. The popularity of units must be introduced into the learning rule, as a result of which a new property of associative memories appears: the robustness of a memory is inverse to the information it conveys. As in some accounts of semantic memory deficits, random damage results in selective impairments, associated to the entropy measure Sf of each memory, since the minimum connectivity required to sustain its retrieval is, in optimal conditions, cM ∝ pSf , and still proportional to pSf but possibly with a larger coefficient in the general case. Present in the entire thesis, but specially in this last Chapter, the conjecture stating that autoassociative memories are limited in the amount of information stored per synapse results consistent with the results.

Hittner, Brian Edward. "Rendering large-scale terrain models and positioning objects in relation to 3D terrain". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Dec%5FHittner.pdf.

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Abstract (sommario):

Thesis (M.S. in Modeling, Virtual Environments and Simulation)--Naval Postgraduate School, December 2003.
Thesis advisor(s): Don Brutzman, Curt Blais. Includes bibliographical references (p. 117-118). Also available online.

Zhao, Ying, e ying zhao@rmit edu au. "Effective Authorship Attribution in Large Document Collections". RMIT University. Computer Science and Information Technology, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080730.162501.

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Techniques that can effectively identify authors of texts are of great importance in scenarios such as detecting plagiarism, and identifying a source of information. A range of attribution approaches has been proposed in recent years, but none of these are particularly satisfactory; some of them are ad hoc and most have defects in terms of scalability, effectiveness, and computational cost. Good test collections are critical for evaluation of authorship attribution (AA) techniques. However, there are no standard benchmarks available in this area; it is almost always the case that researchers have their own test collections. Furthermore, collections that have been explored in AA are usually small, and thus whether the existing approaches are reliable or scalable is unclear. We develop several AA collections that are substantially larger than those in literature; machine learning methods are used to establish the value of using such corpora in AA. The results, also used as baseline results in this thesis, show that the developed text collections can be used as standard benchmarks, and are able to clearly distinguish between different approaches. One of the major contributions is that we propose use of the Kullback-Leibler divergence, a measure of how different two distributions are, to identify authors based on elements of writing style. The results show that our approach is at least as effective as, if not always better than, the best existing attribution methods-that is, support vector machines-for two-class AA, and is superior for multi-class AA. Moreover our proposed method has much lower computational cost and is cheaper to train. Style markers are the key elements of style analysis. We explore several approaches to tokenising documents to extract style markers, examining which marker type works the best. We also propose three systems that boost the AA performance by combining evidence from various marker types, motivated from the observation that there is no one type of marker that can satisfy all AA scenarios. To address the scalability of AA, we propose the novel task of authorship search (AS), inspired by document search and intended for large document collections. Our results show that AS is reasonably effective to find documents by a particular author, even within a collection consisting of half a million documents. Beyond search, we also propose the AS-based method to identify authorship. Our method is substantially more scalable than any method published in prior AA research, in terms of the collection size and the number of candidate authors; the discrimination is scaled up to several hundred authors.

Pan, Bi-Yu. "Hierarchical test generation for VHDL behavioral models". Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-09052009-040449/.

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West, James F. "An examination of the application of design metrics to the development of testing strategies in large-scale SDL models". Virtual Press, 2000. http://liblink.bsu.edu/uhtbin/catkey/1191725.

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There exist a number of well-known and validated design metrics, and the fault prediction available through these metrics has been well documented for systems developed in languages such as C and Ada. However, the mapping and application of these metrics to SDL systems has not been thoroughly explored. The aim of this project is to test the applicability of these metrics in classifying components for testing purposes in a large-scale SDL system. A new model has been developed for this purpose. This research was conducted using a number of SDL systems, most notably actual production models provided by Motorola Corporation.
Department of Computer Science

Kapoor, Shekhar. "Process level test generation for VHDL behavioral models". Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-05022009-040753/.

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Narayanaswamy, Sathyanarayanan. "Development of VHDL behavioral models with back annotated timing". Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06112009-063442/.

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Libri sul tema "Large language models":

Amaratunga, Thimira. Understanding Large Language Models. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7.

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Törnberg, Petter. How to Use Large-Language Models for Text Analysis. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2024. http://dx.doi.org/10.4135/9781529683707.

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Bergsma, Wicher P. Marginal models: For dependent, clustered, and longitudinal categorical data. New York: Springer, 2009.

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Satō, Hideto. A data model, knowledge base, and natural language processing for sharing a large statistical database. Ibaraki, Osaka, Japan: Institute of Social and Economic Research, Osaka University, 1989.

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Bashkatov, Alexander. Modeling in OpenSCAD: examples. ru: INFRA-M Academic Publishing LLC., 2019. http://dx.doi.org/10.12737/959073.

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The tutorial is an introductory course to the study of the basics of geometric modeling for 3D printing using the programming language OpenSCAD and is built on the basis of descriptions of instructions for creating primitives, determining their properties, carrying out transformations and other service operations. It contains a large number of examples with detailed comments and description of the performed actions, which allows you to get basic skills in creating three-dimensional and flat models, exporting and importing graphical data. Meets the requirements of the Federal state educational standards of higher education of the last generation. It can be useful for computer science teachers, students, students and anyone who is interested in three-dimensional modeling and preparation of products for 3D printing.

Greasidis, Thodoris. jQuery design patterns: Learn the best practices on writing efficient jQuery applications to maximize performance in large-scale deployments. Birmingham: Packt Publishing, 2016.

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Robitaille, France, e Marjorie Perreault. Alizé: Vent du large 3. Montréal: Groupe Beauchemin, 2007.

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Vavrenyuk, Aleksandr, Viktor Makarov e Stanislav Kutepov. Operating systems. UNIX bases. ru: INFRA-M Academic Publishing LLC., 2016. http://dx.doi.org/10.12737/11186.

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In the manual basics command interfey-are covered са operating systems of UNIX family. Much attention is paid to practical use of teams of system and opportunities of language programming, shell provided by a cover. In a grant vklyu- Chena also some sections devoted to bases administrirova- niya and to network means of OS. At the end of each section there are questions for self-checking, the appendix contains a large number at - mayors of writing of shell-procedures. The manual is addressed to the students studying the modern information technologies according to programs of a bachelor degree, and also all, who wants to master the OS command interface of family independently UNIX in the shortest possible time. The edition can also be used as the short reference book on wasps - new UNIX OS.

Kublik, Sandra, e Shubham Saboo. Gpt-3: Building Innovative NLP Products Using Large Language Models. O'Reilly Media, Incorporated, 2022.

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Ashwin, Julian, Aditya Chhabra e Vijayendra Rao. Using Large Language Models for Qualitative Analysis can Introduce Serious Bias. World Bank Washington, DC, 2023. http://dx.doi.org/10.1596/1813-9450-10597.

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Capitoli di libri sul tema "Large language models":

McTear, Michael, e Marina Ashurkina. "Large Language Models". In Transforming Conversational AI, 61–84. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0110-5_4.

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Taulli, Tom. "Large Language Models". In Generative AI, 93–125. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_5.

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Vuppalapati, Chandrasekar. "Large Language Models". In International Series in Operations Research & Management Science, 71–131. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56097-2_3.

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Amaratunga, Thimira. "What Makes LLMs Large?" In Understanding Large Language Models, 81–117. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_4.

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Amaratunga, Thimira. "Transformers". In Understanding Large Language Models, 55–79. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_3.

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Amaratunga, Thimira. "NLP Through the Ages". In Understanding Large Language Models, 9–54. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_2.

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Amaratunga, Thimira. "Introduction". In Understanding Large Language Models, 1–7. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_1.

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Amaratunga, Thimira. "Popular LLMs". In Understanding Large Language Models, 119–30. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_5.

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Amaratunga, Thimira. "Threats, Opportunities, and Misconceptions". In Understanding Large Language Models, 131–48. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/979-8-8688-0017-7_6.

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Pires, Ramon, Hugo Abonizio, Thales Sales Almeida e Rodrigo Nogueira. "Sabiá: Portuguese Large Language Models". In Intelligent Systems, 226–40. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45392-2_15.

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Atti di convegni sul tema "Large language models":

Bariah, Lina, Hang Zou, Qiyang Zhao, Belkacem Mouhouche, Faouzi Bader e Merouane Debbah. "Understanding Telecom Language Through Large Language Models". In GLOBECOM 2023 - 2023 IEEE Global Communications Conference. IEEE, 2023. http://dx.doi.org/10.1109/globecom54140.2023.10437725.

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Witteveen, Sam, e Martin Andrews. "Paraphrasing with Large Language Models". In Proceedings of the 3rd Workshop on Neural Generation and Translation. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-5623.

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Debbah, Mérouane. "Large Language Models for Telecom". In 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC). IEEE, 2023. http://dx.doi.org/10.1109/fmec59375.2023.10305960.

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Koyejo, Sanmi, e Bo Li. "Towards Trustworthy Large Language Models". In WSDM '24: The 17th ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3616855.3636454.

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Kodali, Ravi Kishore, Yatendra Prasad Upreti e Lakshmi Boppana. "Large Language Models in AWS". In 2024 1st International Conference on Robotics, Engineering, Science, and Technology (RESTCON). IEEE, 2024. http://dx.doi.org/10.1109/restcon60981.2024.10463557.

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Madasu, Avinash, e Shashank Srivastava. "What do Large Language Models Learn beyond Language?" In Findings of the Association for Computational Linguistics: EMNLP 2022. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-emnlp.516.

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Zhou, Chunfang, Qingyue Gong, Jinyang Zhu e Huidan Luan. "Research and Application of Large Language Models in HealthcareCurrent Development of Large Language Models in the Healthcare FieldA Framework for Applying Large Language Models and the Opportunities and Challenges of Large Language Models in Healthcare: A Framework for Applying Large Language Models and the Opportunities and Challenges of Large Language Models in Healthcare". In ISAIMS 2023: 2023 4th International Symposium on Artificial Intelligence for Medicine Science. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3644116.3644226.

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Deng, Yinlin, Chunqiu Steven Xia, Haoran Peng, Chenyuan Yang e Lingming Zhang. "Large Language Models Are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models". In ISSTA '23: 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3597926.3598067.

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Yang, Yueting, Xintong Zhang, Jinan Xu e Wenjuan Han. "Empowering Vision-Language Models for Reasoning Ability through Large Language Models". In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10446407.

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Todd, Graham, Sam Earle, Muhammad Umair Nasir, Michael Cerny Green e Julian Togelius. "Level Generation Through Large Language Models". In FDG 2023: Foundations of Digital Games 2023. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3582437.3587211.

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Rapporti di organizzazioni sul tema "Large language models":

Prasad, Jayanti. Large Language Models: AI Foundations and Applications in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/85rfezw01y0q9521.

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Abstract (sommario):

This 5-day workshop provides a comprehensive understanding of large language models, their AI foundations, and applications in Python. Designed for PhD students, professors, and professional researchers, the seminar offers hands-on coding sessions, case studies, and discussions on the future of large language models in academic research.

Alonso-Robisco, Andres, e Jose Manuel Carbo. Analysis of CBDC Narrative OF Central Banks using Large Language Models. Madrid: Banco de España, agosto 2023. http://dx.doi.org/10.53479/33412.

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Central banks are increasingly using verbal communication for policymaking, focusing not only on traditional monetary policy, but also on a broad set of topics. One such topic is central bank digital currency (CBDC), which is attracting attention from the international community. The complex nature of this project means that it must be carefully designed to avoid unintended consequences, such as financial instability. We propose the use of different Natural Language Processing (NLP) techniques to better understand central banks’ stance towards CBDC, analyzing a set of central bank discourses from 2016 to 2022. We do this using traditional techniques, such as dictionary-based methods, and two large language models (LLMs), namely Bert and ChatGPT, concluding that LLMs better reflect the stance identified by human experts. In particular, we observe that ChatGPT exhibits a higher degree of alignment because it can capture subtler information than BERT. Our study suggests that LLMs are an effective tool to improve sentiment measurements for policy-specific texts, though they are not infallible and may be subject to new risks, like higher sensitivity to the length of texts, and prompt engineering.

Marra de Artiñano, Ignacio, Franco Riottini Depetris e Christian Volpe Martincus. Automatic Product Classification in International Trade: Machine Learning and Large Language Models. Inter-American Development Bank, luglio 2023. http://dx.doi.org/10.18235/0005012.

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Accurately classifying products is essential in international trade. Virtually all countries categorize products into tariff lines using the Harmonized System (HS) nomenclature for both statistical and duty collection purposes. In this paper, we apply and assess several different algorithms to automatically classify products based on text descriptions. To do so, we use agricultural product descriptions from several public agencies, including customs authorities and the United States Department of Agriculture (USDA). We find that while traditional machine learning (ML) models tend to perform well within the dataset in which they were trained, their precision drops dramatically when implemented outside of it. In contrast, large language models (LLMs) such as GPT 3.5 show a consistently good performance across all datasets, with accuracy rates ranging between 60% and 90% depending on HS aggregation levels. Our analysis highlights the valuable role that artificial intelligence (AI) can play in facilitating product classification at scale and, more generally, in enhancing the categorization of unstructured data.

Horton, John. Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus? Cambridge, MA: National Bureau of Economic Research, aprile 2023. http://dx.doi.org/10.3386/w31122.

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Gluckman, Peter, e Hema Sridhar. A framework for evaluating rapidly developing digital and related technologies: AI, Large Language Models and beyond. International Science Council, ottobre 2023. http://dx.doi.org/10.24948/2023.11.

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Gluckman, Peter, e Hema Sridhar. A guide for policy-makers: Evaluating rapidly developing technologies including AI, large language models and beyond. International Science Council, aprile 2024. http://dx.doi.org/10.24948/2024.07.

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Zhu, Minjie, e Michael Scott. Fluid-Structure Interaction and Python-Scripting Capabilities in OpenSees. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, agosto 2019. http://dx.doi.org/10.55461/vdix3057.

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Building upon recent advances in OpenSees, the goals of this project are to expand the framework’s Python scripting capabilities and to further develop its fluid–structure interaction (FSI) simulation capabilities, which are based on the particle finite-element method (PFEM). At its inception, the FSI modules in OpenSees were based on Python scripting. To accomplish FSI simulations in OpenSees, Python commands have been added for a limited number of pre-existing element and material commands, e.g., linear-elastic triangle elements and beam–column elements with Concrete01/Steel01 fiber sections. Incorporation of hundreds of constitutive models and element formulations under the Python umbrella for FSI and general OpenSees use remain to be done. Although the original scripting language, Tcl, in OpenSees is string based, powerful, and easy to learn, it is not suitable for mathematical computations. Recent trends in scripting languages for engineering applications have embraced more general, scientific languages such as Python, which has evolved to a large community with numerous libraries for numerical computing, data analysis, scientific visualization, and web development. These libraries can be utilized with the FSI simulation for tsunami analysis. Extending OpenSees to Python will help OpenSees keep pace with new scripting developments from the scientific computing community and make the framework more accessible to graduate students, who likely have learned Python as undergraduates.

Seymore, Kristie, e Ronald Rosenfeld. Large-Scale Topic Detection and Language Model Adaptation. Fort Belvoir, VA: Defense Technical Information Center, giugno 1997. http://dx.doi.org/10.21236/ada327553.

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Zhang, Hao. Large Language Model (LLM) Monthly Report (2024 Apr). ResearchHub Technologies, Inc., maggio 2024. http://dx.doi.org/10.55277/researchhub.0ps6xenm.

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Fischer, Eric, Rebecca McCaughrin, Saketh Prazad e Mark Vandergon. Fed Transparency and Policy Expectation Errors: A Text Analysis Approach. Federal Reserve Bank of New York, novembre 2023. http://dx.doi.org/10.59576/sr.1081.

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This paper seeks to estimate the extent to which market-implied policy expectations could be improved with further information disclosure from the FOMC. Using text analysis methods based on large language models, we show that if FOMC meeting materials with five-year lagged release dates—like meeting transcripts and Tealbooks—were accessible to the public in real time, market policy expectations could substantially improve forecasting accuracy. Most of this improvement occurs during easing cycles. For instance, at the six-month forecasting horizon, the market could have predicted as much as 125 basis points of additional easing during the 2001 and 2008 recessions, equivalent to a 40-50 percent reduction in mean squared error. This potential forecasting improvement appears to be related to incomplete information about the Fed’s reaction function, particularly with respect to financial stability concerns in 2008. In contrast, having enhanced access to meeting materials would not have improved the market’s policy rate forecasting during tightening cycles.