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Добірка наукової літератури з теми "Large Language Models (LLMs)"

Автор: Grafiati

Опубліковано: 18 листопада 2024

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Статті в журналах з теми "Large Language Models (LLMs)"

Zhang, Tianyi, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, and Tatsunori B. Hashimoto. "Benchmarking Large Language Models for News Summarization." Transactions of the Association for Computational Linguistics 12 (2024): 39–57. http://dx.doi.org/10.1162/tacl_a_00632.

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Abstract Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood. By conducting a human evaluation on ten LLMs across different pretraining methods, prompts, and model scales, we make two important observations. First, we find instruction tuning, not model size, is the key to the LLM’s zero-shot summarization capability. Second, existing studies have been limited by low-quality references, leading to underestimates of human performance and lower few-shot and finetuning performance. To better evaluate LLMs, we perform human evaluation over high-quality summaries we collect from freelance writers. Despite major stylistic differences such as the amount of paraphrasing, we find that LLM summaries are judged to be on par with human written summaries.

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Hamaniuk, Vita A. "The potential of Large Language Models in language education." Educational Dimension 5 (December 9, 2021): 208–10. http://dx.doi.org/10.31812/ed.650.

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This editorial explores the potential of Large Language Models (LLMs) in language education. It discusses the role of LLMs in machine translation, the concept of ‘prompt programming’, and the inductive bias of LLMs for abstract textual reasoning. The editorial also highlights using LLMs as creative writing tools and their effectiveness in paraphrasing tasks. It concludes by emphasizing the need for responsible and ethical use of these tools in language education.

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Yang, Jidong. "Large language models privacy and security." Applied and Computational Engineering 76, no. 1 (July 16, 2024): 177–88. http://dx.doi.org/10.54254/2755-2721/76/20240584.

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The advancement of large language models (LLMs) has yielded significant advancements across various domains. Nevertheless, this progress has also raised crucial concerns regarding privacy and security. The paper does a comprehensive literature study to thoroughly examine the fundamental principles of LLM. It also provides a detailed examination of the characteristics and application fields of various LLMs, with a particular focus on Transformer. Furthermore, this study places emphasis on the examination of privacy concerns that may emerge in the context of LLM's handling of personal and sensitive data. It also explores the potential hazards associated with information leakage and misuse, as well as the existing privacy safeguards and the obstacles encountered in their implementation. Overall, LLM has made significant advancements in technology. However, it is imperative to acknowledge the importance of doing research on safeguarding privacy and enhancing security. These aspects are vital for guaranteeing the sustained development and public confidence in LLM technology.

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Huang, Dawei, Chuan Yan, Qing Li, and Xiaojiang Peng. "From Large Language Models to Large Multimodal Models: A Literature Review." Applied Sciences 14, no. 12 (June 11, 2024): 5068. http://dx.doi.org/10.3390/app14125068.

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With the deepening of research on Large Language Models (LLMs), significant progress has been made in recent years on the development of Large Multimodal Models (LMMs), which are gradually moving toward Artificial General Intelligence. This paper aims to summarize the recent progress from LLMs to LMMs in a comprehensive and unified way. First, we start with LLMs and outline various conceptual frameworks and key techniques. Then, we focus on the architectural components, training strategies, fine-tuning guidance, and prompt engineering of LMMs, and present a taxonomy of the latest vision–language LMMs. Finally, we provide a summary of both LLMs and LMMs from a unified perspective, make an analysis of the development status of large-scale models in the view of globalization, and offer potential research directions for large-scale models.

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Kumar, Deepak, Yousef Anees AbuHashem, and Zakir Durumeric. "Watch Your Language: Investigating Content Moderation with Large Language Models." Proceedings of the International AAAI Conference on Web and Social Media 18 (May 28, 2024): 865–78. http://dx.doi.org/10.1609/icwsm.v18i1.31358.

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Large language models (LLMs) have exploded in popularity due to their ability to perform a wide array of natural language tasks. Text-based content moderation is one LLM use case that has received recent enthusiasm, however, there is little research investigating how LLMs can help in content moderation settings. In this work, we evaluate a suite of commodity LLMs on two common content moderation tasks: rule-based community moderation and toxic content detection. For rule-based community moderation, we instantiate 95 subcommunity specific LLMs by prompting GPT-3.5 with rules from 95 Reddit subcommunities. We find that GPT-3.5 is effective at rule-based moderation for many communities, achieving a median accuracy of 64% and a median precision of 83%. For toxicity detection, we evaluate a range of LLMs (GPT-3, GPT-3.5, GPT-4, Gemini Pro, LLAMA 2) and show that LLMs significantly outperform currently widespread toxicity classifiers. However, we also found that increases in model size add only marginal benefit to toxicity detection, suggesting a potential performance plateau for LLMs on toxicity detection tasks. We conclude by outlining avenues for future work in studying LLMs and content moderation.

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Pendyala, Vishnu S., and Christopher E. Hall. "Explaining Misinformation Detection Using Large Language Models." Electronics 13, no. 9 (April 26, 2024): 1673. http://dx.doi.org/10.3390/electronics13091673.

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Large language models (LLMs) are a compressed repository of a vast corpus of valuable information on which they are trained. Therefore, this work hypothesizes that LLMs such as Llama, Orca, Falcon, and Mistral can be used for misinformation detection by making them cross-check new information with the repository on which they are trained. Accordingly, this paper describes the findings from the investigation of the abilities of LLMs in detecting misinformation on multiple datasets. The results are interpreted using explainable AI techniques such as Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Integrated Gradients. The LLMs themselves are also asked to explain their classification. These complementary approaches aid in better understanding the inner workings of misinformation detection using LLMs and lead to conclusions about their effectiveness at the task. The methodology is generic and nothing specific is assumed for any of the LLMs, so the conclusions apply generally. Primarily, when it comes to misinformation detection, the experiments show that the LLMs are limited by the data on which they are trained.

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Cheng, Jerome. "Applications of Large Language Models in Pathology." Bioengineering 11, no. 4 (March 31, 2024): 342. http://dx.doi.org/10.3390/bioengineering11040342.

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Large language models (LLMs) are transformer-based neural networks that can provide human-like responses to questions and instructions. LLMs can generate educational material, summarize text, extract structured data from free text, create reports, write programs, and potentially assist in case sign-out. LLMs combined with vision models can assist in interpreting histopathology images. LLMs have immense potential in transforming pathology practice and education, but these models are not infallible, so any artificial intelligence generated content must be verified with reputable sources. Caution must be exercised on how these models are integrated into clinical practice, as these models can produce hallucinations and incorrect results, and an over-reliance on artificial intelligence may lead to de-skilling and automation bias. This review paper provides a brief history of LLMs and highlights several use cases for LLMs in the field of pathology.

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Chu, Zhibo, Zichong Wang, and Wenbin Zhang. "Fairness in Large Language Models: A Taxonomic Survey." ACM SIGKDD Explorations Newsletter 26, no. 1 (July 24, 2024): 34–48. http://dx.doi.org/10.1145/3682112.3682117.

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Large Language Models (LLMs) have demonstrated remarkable success across various domains. However, despite their promising performance in numerous real-world applications, most of these algorithms lack fairness considerations. Consequently, they may lead to discriminatory outcomes against certain communities, particularly marginalized populations, prompting extensive study in fair LLMs. On the other hand, fairness in LLMs, in contrast to fairness in traditional machine learning, entails exclusive backgrounds, taxonomies, and fulfillment techniques. To this end, this survey presents a comprehensive overview of recent advances in the existing literature concerning fair LLMs. Specifically, a brief introduction to LLMs is provided, followed by an analysis of factors contributing to bias in LLMs. Additionally, the concept of fairness in LLMs is discussed categorically, summarizing metrics for evaluating bias in LLMs and existing algorithms for promoting fairness. Furthermore, resources for evaluating bias in LLMs, including toolkits and datasets, are summarized. Finally, existing research challenges and open questions are discussed.

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Lin, Hsiao-Ying, and Jeffrey Voas. "Lower Energy Large Language Models (LLMs)." Computer 56, no. 10 (October 2023): 14–16. http://dx.doi.org/10.1109/mc.2023.3278160.

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Long, Robert. "Introspective Capabilities in Large Language Models." Journal of Consciousness Studies 30, no. 9 (September 30, 2023): 143–53. http://dx.doi.org/10.53765/20512201.30.9.143.

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This paper considers the kind of introspection that large language models (LLMs) might be able to have. It argues that LLMs, while currently limited in their introspective capabilities, are not inherently unable to have such capabilities: they already model the world, including mental concepts, and already have some introspection-like capabilities. With deliberate training, LLMs may develop introspective capabilities. The paper proposes a method for such training for introspection, situates possible LLM introspection in the 'possible forms of introspection' framework proposed by Kammerer and Frankish, and considers the ethical ramifications of introspection and self-report in AI systems.

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Дисертації з теми "Large Language Models (LLMs)"

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

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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

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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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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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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.

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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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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.

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Zhao, Ying, and 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.

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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

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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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Книги з теми "Large Language Models (LLMs)"

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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Martra, Pere. Large Language Models Projects. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0515-8.

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Kucharavy, Andrei, Octave Plancherel, Valentin Mulder, Alain Mermoud, and Vincent Lenders, eds. Large Language Models in Cybersecurity. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7.

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Kamath, Uday, Kevin Keenan, Garrett Somers, and Sarah Sorenson. Large Language Models: A Deep Dive. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65647-7.

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Grigorov, Dilyan. Introduction to Python and Large Language Models. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0540-0.

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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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King, Susan. Waking the princess. Waterville, Me: Wheeler Pub., 2004.

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King, Susan. La princesa dormida. Barcelona: Ediciones Urano, 2010.

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King, Susan. Waking the princess. New York: New American Library, 2003.

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Lewis, Carroll. Through the looking glass. San Diego, CA: ICON Classics, 2005.

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Частини книг з теми "Large Language Models (LLMs)"

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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Ruiu, Dragos. "LLMs Red Teaming." In Large Language Models in Cybersecurity, 213–23. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7_24.

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AbstractPrompt red-teaming is a form of evaluation that involves testing machine learning models for vulnerabilities that could result in undesirable behaviors. It is similar to adversarial attacks, but red-teaming prompts appear like regular natural language prompts, and they reveal model limitations that can cause harmful user experiences or aid violence. Red-teaming can be resource-intensive due to the large search space required to search the prompt space of possible model failures. Augmenting the model with a classifier trained to predict potentially undesirable texts is a possible workaround. Red-teaming LLMs is a developing research area, and there is a need for best practices, including persuading people to harm themselves or others and other problematic behaviors, such as memorization, spam, weapons assembly instructions, and the generation of code with pre-defined vulnerabilities. The challenge with evaluating LLMs for malicious behaviors is that they are not explicitly trained to exhibit such behaviors. Therefore, it is critical to continually develop red-teaming methods that can adapt as models become more powerful. Multi-organization collaboration on datasets and best practices can enable smaller entities releasing models to still red-team their models before release, leading to a safer user experience across the board.

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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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Martra, Pere. "Vector Databases and LLMs." In Large Language Models Projects, 31–62. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0515-8_2.

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Kucharavy, Andrei. "Overview of Existing LLM Families." In Large Language Models in Cybersecurity, 31–44. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7_3.

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AbstractWhile the general public discovered Large Language Models (LLMs) with ChatGPT—a generative autoregressive model, they are far from the only models in the LLM family. Various architectures and training regiments optimized for specific usages were designed throughout their development, which were then classified as different LLM families.

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Würsch, Maxime, Dimitri Percia David, and Alain Mermoud. "Monitoring Emerging Trends in LLM Research." In Large Language Models in Cybersecurity, 153–61. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7_17.

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AbstractEstablished methodologies for monitoring and forecasting trends in technological development fall short of capturing advancements in Large Language Models (LLMs). This chapter suggests a complementary and alternative approach to mitigate this concern. Traditional indicators, such as search volumes and citation frequencies, are demonstrated to inadequately reflect the rapid evolution of LLM-related technologies due to biases, semantic drifts, and inherent lags in data documentation. Our presented methodology analyzes the proximity of technological terms related to LLMs, leveraging the OpenAlex and arXiv databases, and focuses on extracting nouns from scientific papers to provide a nuanced portrayal of advancements in LLM technologies. The approach aims to counteract the inherent lags in data, accommodate semantic drift, and distinctly differentiate between various topics, offering both retrospective and prospective insights in their analytical purview. The insights derived underline the need for refined, robust, adaptable, and precise forecasting models as LLMs intersect with domains like cyber defense. At the same time, they are considering the limitations of singular ontologies and integrating advanced anticipatory measures for a nuanced understanding of evolving LLM technologies.

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Kamath, Uday, Kevin Keenan, Garrett Somers, and Sarah Sorenson. "Multimodal LLMs." In Large Language Models: A Deep Dive, 375–421. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65647-7_9.

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Kucharavy, Andrei. "Fundamental Limitations of Generative LLMs." In Large Language Models in Cybersecurity, 55–64. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7_5.

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AbstractGiven the impressive performances of LLM-derived tools across a range of tasks considered all but impossible for computers until recently, the capabilities of LLMs seem limitless. However, there are some fundamental limitations to what they can or cannot do inherent to the current architecture of LLMs. I will attempt to review the most notable of them to give the reader an understanding of what architectural modifications will need to take place before a given problem is solved. Specifically, I discuss counterfactual generation, private information leakage, reasoning, limited attention span, dependence on the training dataset, bias, and non-normative language.

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Meier, Raphael. "LLM-Aided Social Media Influence Operations." In Large Language Models in Cybersecurity, 105–12. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7_11.

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AbstractSocial media platforms enable largely unrestricted many-to-many communication. In times of crisis, they offer a space for collective sense-making and give rise to new social phenomena (e.g., open-source investigations). However, they also serve as a tool for threat actors to conduct Cyber-enabled Social Influence Operations (CeSIOs) to shape public opinion and interfere in decision-making processes. CeSIOs employ sock puppet accounts to engage authentic users in online communication, exert influence, and subvert online discourse. Large Language Models (LLMs) may further enhance the deceptive properties of sock puppet accounts. Recent LLMs can generate targeted and persuasive text, which is, for the most part, indistinguishable from human-written content—ideal features for covert influence. This article reviews recent developments at the intersection of LLMs and influence operations, summarizes LLMs’ salience, and explores the potential impact of LLM-instrumented sock puppet accounts for CeSIOs. Finally, mitigation measures for the near future are highlighted.

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Schillaci, Zachary. "LLM Adoption Trends and Associated Risks." In Large Language Models in Cybersecurity, 121–28. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54827-7_13.

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AbstractThe emergence of Large Language Models (LLMs) is expected to impact the job market significantly, accelerating automation trends and posing a risk to traditionally creative-oriented jobs. LLMs can automate tasks in various fields, including design, journalism, and creative writing. Companies and public institutions can leverage generative models to enhance productivity and reduce workforce requirements through machine-assisted workflows and natural language interactions. While technical skills like programming may become less important in certain roles, generative models are unlikely to fully replace programmers due to the need for expertise in code validation and niche development. The enterprise landscape of LLMs comprises providers (organizations training proprietary models), integrators (technology companies fine-tuning LLMs for specific applications), and users (companies and individuals adopting LLM-powered solutions). The applications of the models include conversational search, customer service chatbots, content creation, personalized marketing, data analysis, and basic workflow automation. The regulatory landscape is rapidly evolving, with key considerations including copyright, data security, and liability. Government involvement and informed expertise are recommended to guide governance and decision-making processes in this domain.

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Тези доповідей конференцій з теми "Large Language Models (LLMs)"

Coppolillo, Erica, Francesco Calimeri, Giuseppe Manco, Simona Perri, and Francesco Ricca. "LLASP: Fine-tuning Large Language Models for Answer Set Programming." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}, 834–44. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/78.

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Recently, Large Language Models (LLMs) have showcased their potential in various natural language processing tasks, including code generation. However, while significant progress has been made in adapting LLMs to generate code for several imperative programming languages and tasks, there remains a notable gap in their application to declarative formalisms, such as Answer Set Programming (ASP). In this paper, we move a step towards exploring the capabilities of LLMs for ASP code generation. First, we perform a systematic evaluation of several state-of-the-art LLMs. Despite their power in terms of number of parameters, training data and computational resources, empirical results demonstrate inadequate performances in generating correct ASP programs. Therefore, we propose LLASP, a fine-tuned lightweight model specifically trained to encode fundamental ASP program patterns. To this aim, we create an ad-hoc dataset covering a wide variety of fundamental problem specifications that can be encoded in ASP. Our experiments demonstrate that the quality of ASP programs generated by LLASP is remarkable. This holds true not only when compared to the non-fine-tuned counterpart but also when compared to the majority of eager LLM candidates, particularly from a semantic perspective. All the code and data used to perform the experiments are publicly available: https://github.com/EricaCoppolillo/LLASP.

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Zhang, Duzhen, Yahan Yu, Jiahua Dong, Chenxing Li, Dan Su, Chenhui Chu, and Dong Yu. "MM-LLMs: Recent Advances in MultiModal Large Language Models." In Findings of the Association for Computational Linguistics ACL 2024, 12401–30. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.738.

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Zolnai-Lucas, Aaron, Jack Boylan, Chris Hokamp, and Parsa Ghaffari. "STAGE: Simplified Text-Attributed Graph Embeddings using Pre-trained LLMs." In Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024), 92–104. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.kallm-1.10.

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Barek, MD Abdul, Md Mostafizur Rahman, Shapna Akter, A. B. M. Kamrul Islam Riad, Md Abdur Rahman, Hossain Shahriar, Akond Rahman, and Fan Wu. "Mitigating Insecure Outputs in Large Language Models(LLMs): A Practical Educational Module." In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), 2424–29. IEEE, 2024. http://dx.doi.org/10.1109/compsac61105.2024.00389.

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Kotkar, Aishwarya D., Radhakrushna S. Mahadik, Piyush G. More, and Sandeep A. Thorat. "Comparative Analysis of Transformer-based Large Language Models (LLMs) for Text Summarization." In 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/acet61898.2024.10730348.

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Gurgurov, Daniil, Mareike Hartmann, and Simon Ostermann. "Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters." In Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024), 63–74. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.kallm-1.7.

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Wasi, Azmine Toushik. "HRGraph: Leveraging LLMs for HR Data Knowledge Graphs with Information Propagation-based Job Recommendation." In Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024), 56–62. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.kallm-1.6.

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Cao, Ye. "Optimizing Style Recognition Algorithm for Digital Art Images Using Large Language Models (LLMs)." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC), 1536–42. IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10689850.

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Alipour, Hanieh, Nick Pendar, and Kohinoor Roy. "ChatGPT Alternative Solutions: Large Language Models Survey." In 5th International Conference on Networks, Blockchain and Internet of Things. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.1405114.

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In recent times, the grandeur of Large Language Models (LLMs) has not only shone in the realm of natural language processing but has also cast its brilliance across a vast array of applications. This remarkable display of LLM capabilities has ignited a surge in research contributions within this domain, spanning a diverse spectrum of topics. These contributions encompass advancements in neural network architecture, context length enhancements, model alignment, training datasets, benchmarking, efficiency improvements, and more. Recent years have witnessed a dynamic synergy between academia and industry, propelling the field of LLM research to new heights. A notable milestone in this journey is the introduction of ChatGPT, a powerful AI chatbot grounded in LLMs, which has garnered widespread societal attention. The evolving technology of LLMs has begun to reshape the landscape of the entire AI community, promising a revolutionary shift in the way we create and employ AI algorithms. Given this swift-paced technical evolution, our survey embarks on a journey to encapsulate the recent strides made in the world of LLMs. Through an exploration of the background, key discoveries, and prevailing methodologies, we offer an up-to-theminute review of the literature. By examining multiple LLM models, our paper not only presents a comprehensive overview but also charts a course that identifies existing challenges and points toward potential future research trajectories. This survey furnishes a well-rounded perspective on the current state of generative AI, shedding light on opportunities for further exploration, enhancement, and innovation.

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Ma, Kevin, Daniele Grandi, Christopher McComb, and Kosa Goucher-Lambert. "Conceptual Design Generation Using Large Language Models." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-116838.

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Abstract Concept generation is a creative step in the conceptual design phase, where designers often turn to brainstorming, mindmapping, or crowdsourcing design ideas to complement their own knowledge of the domain. Recent advances in natural language processing (NLP) and machine learning (ML) have led to the rise of Large Language Models (LLMs) capable of generating seemingly creative outputs from textual prompts. The success of these models has led to their integration and application across a variety of domains, including art, entertainment, and other creative work. In this paper, we leverage LLMs to generate solutions for a set of 12 design problems and compare them to a baseline of crowdsourced solutions. We evaluate the differences between generated and crowdsourced design solutions through multiple perspectives, including human expert evaluations and computational metrics. Expert evaluations indicate that the LLM-generated solutions have higher average feasibility and usefulness while the crowdsourced solutions have more novelty. We experiment with prompt engineering and find that leveraging few-shot learning can lead to the generation of solutions that are more similar to the crowdsourced solutions. These findings provide insight into the quality of design solutions generated with LLMs and begins to evaluate prompt engineering techniques that could be leveraged by practitioners to generate higher-quality design solutions synergistically with LLMs.

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Звіти організацій з теми "Large Language Models (LLMs)"

Alonso-Robisco, Andres, and Jose Manuel Carbo. Analysis of CBDC Narrative OF Central Banks using Large Language Models. Madrid: Banco de España, August 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.

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Marra de Artiñano, Ignacio, Franco Riottini Depetris, and Christian Volpe Martincus. Automatic Product Classification in International Trade: Machine Learning and Large Language Models. Inter-American Development Bank, July 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.

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Maerz, Seraphine. Using AI for Text Analysis in R. Instats Inc., 2024. http://dx.doi.org/10.61700/ti5uexui5ilrd1663.

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This one-day workshop provides a basic introduction to using artificial intelligence for text analysis. Tailored for researchers across a variety of social and health science fields, participants will gain practical skills in building text corpuses, topic modeling, and using ChatGPT, Copilot, and other Large Language Models (LLMs) in R, while addressing ethical considerations and validation techniques for AI-driven research.

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Azuara Herrera, Oliver, Laura Ripani, and Eric Torres Ramirez. AI and the Increase of Productivity and Labor Inequality in Latin America: Potential Impact of Large Language Models on Latin American Workforce. Inter-American Development Bank, September 2024. http://dx.doi.org/10.18235/0013152.

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We assess the potential effect of large language models (LLMs) on the labor markets of Chile, Mexico, and Peru using the methodology of Eloundou et al. (2023). This approach involves detailed guidelines (rubrics) for each job to assess whether access to LMM software would reduce the time required for workers to complete their daily tasks. Adapting this methodology to the Latin American context necessitated developing a comprehensive crosswalk between the Occupational Information Network (O*NET) and regional occupational classifications, SINCO-2011 and ISCO-2008. When we use this adaptation, the theoretical average task exposure of occupations under these classifications is 32% and 31% for each classification. Recognizing the unique characteristics of each country's labor market, we refined these ratings to reflect better each nation's capacity to adopt and effectively implement new technologies. After these adjustments, the task exposure for SINCO-2011 drops to 27% and for ISCO-2008 to 23%. These adjusted exposure ratings provide a more accurate depiction of the real-world implications of LLM integration in the Latin American context. According to this methodology, the LLM-powered exposure using GPT-4 estimates suggests that the percentage of jobs with task exposure exceeding 10% is 74% in Mexico, 76% in Chile, and 76% in Peru. When we raise the exposure threshold to 40% or more, the proportion of affected occupations significantly decreases to 9% in Mexico, 20% in Chile, and 6% in Peru. The exposure is close to zero after this threshold. In other words, the exposure would only affect less than half of the total labor force in these countries. Further analysis of exposure by socioeconomic conditions indicates higher exposure among women, individuals with higher education, formal employees, and higher-income groups. This suggests a potential increase in labor inequality in the region due to adopting this technology. Our findings highlight the need for targeted policy interventions and adaptive strategies to ensure that the transition to an AI-enhanced labor market benefits all socio-economic groups and minimizes disruptions.

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Korinek, Anton, and Jai Vipra. Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence. Institute for New Economic Thinking Working Paper Series, October 2024. http://dx.doi.org/10.36687/inetwp228.

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This paper examines the evolving structure and competition dynamics of the rapidly growing market for foundation models, focusing on large language models (LLMs). We describe the technological characteristics that shape the industry and have given rise to fierce competition among the leading players. The paper analyzes the cost structure of foundation models, emphasizing the importance of key inputs such as computational resources, data, and talent, and identifies significant economies of scale and scope that may create a tendency towards greater market concentration in the future. We explore two concerns for competition, the risk of market tipping and the implications of vertical integration, and use our analysis to inform policy remedies to maintain a competitive landscape.

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Wagner, Rudolf. Enforcing Software and AI as Medical Devices: Expert Witness Insights on Civil Lawsuits, Regulation, and Legal Liability Pathways. ADHOCON UG (haftungsbeschränkt), October 2024. http://dx.doi.org/10.70317/2024.13rw10.

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As an expert witness to German courts (Landgerichte, Oberlandesgerichte) and federal courts for Medical Devices and IVDs the number of expert statements has constantly increased, especially for Software and AI in Healthcare and focusing on their Medical Device classification. The paper also examines how competition and insurers may respond to claims arising from the use of improper classified software, artificial intelligence including Large Language Models (LLMs) with denial of coverage, increased premiums, and subrogation actions against hospitals or AI developers. Regulatory challenges are discussed in light of Software as a Medical Device (SaMD) frameworks, highlighting the effective and valid regulations to classify the risk of Software and AI when used in Healthcare, which are enforced today although the dynamic and evolving nature of AI systems. This paper concludes the used regulatory and legal pathways used in cases with expert witness statements between competitors or by competitors to file a lawsuit against a competitor with incorrect classification based on experience and draws the parallels to the US FDA regulation. Beyond it demonstrates that authority driven enforcement of existing regulation is transferred to courts acting on behalf of the authority driven by compliant legal manufacturers.

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Wagner, Rudolf. Enforcing Software and AI as Medical Devices: Expert Witness Insights on Civil Lawsuits, Regulation, and Legal Liability Pathways. ADHOCON UG (haftungsbeschraenkt), October 2024. http://dx.doi.org/10.70317/2024.20rw10.

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

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Tranchero, Matteo, Cecil-Francis Brenninkmeijer, Arul Murugan, and Abhishek Nagaraj. Theorizing with Large Language Models. Cambridge, MA: National Bureau of Economic Research, October 2024. http://dx.doi.org/10.3386/w33033.

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Moreno, Ángel Iván, and Teresa Caminero. Assessing the data challenges of climate-related disclosures in european banks. A text mining study. Madrid: Banco de España, September 2023. http://dx.doi.org/10.53479/33752.

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The Intergovernmental Panel on Climate Change (IPCC) estimates that global net-zero should be achieved by 2050. To this end, many private firms are pledging to reach net-zero emissions by 2050. The Climate Data Steering Committee (CDSC) is working on an initiative to create a global central digital repository of climate disclosures, which aims to address the current data challenges. This paper assesses the progress within European financial institutions towards overcoming the data challenges outlined by the CDSC. Using a text-mining approach, coupled with the application of commercial Large Language Models (LLM) for context verification, we calculate a Greenhouse Gas Disclosure Index (GHGDI), by analysing 23 highly granular disclosures in the ESG reports between 2019 and 2021 of most of the significant banks under the ECB’s direct supervision. This index is then compared with the CDP score. The results indicate a moderate correlation between institutions not reporting to CDP upon request and a low GHGDI. Institutions with a high CDP score do not necessarily correlate with a high GHGDI.

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